Idea Machines

Tim Hwang turns the tables and interviews me (Ben) about Speculative Technologies and research management. 

Peter van Hardenberg talks about Industrialists vs. Academics, Ink&Switch's evolution over time, the Hollywood Model, internal lab infrastructure, and more! Peter is the lab director and CEO of Ink&Switch, a private, creator oriented, computing research lab.  References Ink&Switch (and their many publications) The Hollywood Model in R&D Idea Machines Episode with Adam Wiggins Paul Erdós Transcript Peter Van Hardenberg [00:01:21] Ben: Today I have the pleasure of speaking with Peter van Hardenbergh. Peter is the lab director and CEO of Inkin switch. Private creator oriented, competing research lab. I talked to Adam Wiggins, one of inkind switches founders, [00:01:35] way back in episode number four. It's amazing to see the progress they've made as an organization. They've built up an incredible community of fellow travelers and consistently released research reports that gesture at possibilities for competing that are orthogonal to the current hype cycles. Peter frequently destroys my complacency with his ability to step outside the way that research has normally done and ask, how should we be operating, given our constraints and goals. I hope you enjoy my conversation with Peter. Would you break down your distinction between academics and industrialists [00:02:08] Peter: Okay. Academics are people whose incentive structure is connected to the institutional rewards of the publishing industry, right? You, you publish papers. And you get tenure and like, it's a, it's, it's not so cynical or reductive, but like fundamentally the time cycles are long, right? Like you have to finish work according to when, you know, submission deadlines for a conference are, you know, you're [00:02:35] working on something now. You might come back to it next quarter or next year or in five years, right? Whereas when you're in industry, you're connected to users, you're connected to people at the end of the day who need to touch and hold and use the thing. And you know, you have to get money from them to keep going. And so you have a very different perspective on like time and money and space and what's possible. And the real challenge in terms of connecting these two, you know, I didn't invent the idea of pace layers, right? They, they operate at different pace layers. Academia is often intergenerational, right? Whereas industry is like, you have to make enough money every quarter. To keep the bank account from going below zero or everybody goes home, [00:03:17] Ben: Right. Did. Was it Stuart Brand who invented pace [00:03:22] Peter: believe it was Stewart Brand. Pace layers. Yeah. [00:03:25] Ben: That actually I, I'd never put these two them together, but the, the idea I, I, I think about impedance mismatches between [00:03:35] organizations a lot. And that really sort of like clicks with pace layers Exactly. Right. Where it's like [00:03:39] Peter: Yeah, absolutely. And, and I think in a big way what we're doing at, Ink& Switch on some level is trying to provide like synchro mesh between academia and industry, right? Because they, the academics are moving on a time scale and with an ambition that's hard for industry to match, right? But also, Academics. Often I think in computer science are like, have a shortage of good understanding about what the real problems people are facing in the world today are. They're not disinterested. [00:04:07] Ben: just computer [00:04:08] Peter: Those communication channels don't exist cuz they don't speak the same language, they don't use the same terminology, they don't go to the same conferences, they don't read the same publications. Right. [00:04:18] Ben: Yeah. [00:04:18] Peter: so vice versa, you know, we find things in industry that are problems and then it's like you go read the papers and talk to some scientists. I was like, oh dang. Like. We know how to solve this. It's just nobody's built it. [00:04:31] Ben: Yeah. [00:04:32] Peter: Or more accurately it would be to say [00:04:35] there's a pretty good hunch here about something that might work, and maybe we can connect the two ends of this together. [00:04:42] Ben: Yeah. Often, I, I think of it as someone, someone has, it is a quote unquote solved problem, but there are a lot of quote unquote, implementation details and those implementation details require a year of work. [00:04:56] Peter: yeah, a year or many years? Or an entire startup, or a whole career or two? Yeah. And, and speaking of, Ink&Switch, I don't know if we've ever talked about, so a switch has been around for more than half a decade, right? [00:05:14] Peter: Yeah, seven or eight years now, I think I could probably get the exact number, but yeah, about that. [00:05:19] Ben: And. I think I don't have a good idea in my head over that time. What, what has changed about in, can switches, conception of itself and like how you do things. Like what is, what are some of the biggest things that have have changed over that time?[00:05:35] [00:05:35] Peter: So I think a lot of it could be summarized as professionalization. But I, I'll give a little brief history and can switch began because the. You know, original members of the lab wanted to do a startup that was Adam James and Orion, but they recognized that they didn't, they weren't happy with computing and where computers were, and they knew that they wanted to make something that would be a tool that would help people who were solving the world's problems work better. That's kinda a vague one, but You know, they were like, well, we're not physicists, we're not social scientists. You know, we can't solve climate change or radicalization directly, or you know, the journalism crisis or whatever, but maybe we can build tools, right? We know how to make software tools. Let's build tools for the people who are solving the problems. Because right now a lot of those systems they rely on are getting like steadily worse every day. And I think they still are like the move to the cloud disempowerment of the individual, like, you [00:06:35] know, surveillance technology, distraction technology. And Tristan Harris is out there now. Like hammering on some of these points. But there's just a lot of things that are like slow and fragile and bad and not fun to work with and lose your, you know, lose your work product. You know, [00:06:51] Ben: Yeah, software as a service more generally. [00:06:54] Peter: Yeah. And like, there's definitely advantages. It's not like, you know, people are rational actors, but something was lost. And so the idea was well go do a bit of research, figure out what the shape of the company is, and then just start a company and, you know, get it all solved and move on. And I think the biggest difference, at least, you know, aside from scale and like actual knowledge is just kind of the dawning realization at some point that like there won't really be an end state to this problem. Like this isn't a thing that's transitional where you kind of come in and you do some research for a bit, and then we figure out the answer and like fold up the card table and move on to the next thing. It's like, oh no, this, this thing's gotta stick around because these problems aren't gonna [00:07:35] go away. And when we get through this round of problems, we already see what the next round are. And that's probably gonna go on for longer than any of us will be working. And so the vision now, at least from my perspective as the current lab director, is much more like, how can I get this thing to a place where it can sustain for 10 years, for 50 years, however long it takes, and you know, to become a place that. Has a culture that can sustain, you know, grow and change as new people come in. But that can sustain operations indefinitely. [00:08:07] Ben: Yeah. And, and so to circle back to the. The, the jumping off point for this, which is sort of since, since it began, what have been some of the biggest changes of how you operate? How you, or just like the, the model more generally or, or things that you were [00:08:30] Peter: Yeah, so the beginning was very informal, but, so maybe I'll skip over the first like [00:08:35] little period where it was just sort of like, Finding our footing. But around the time when I joined, we were just four or five people. And we did one project, all of us together at a time, and we just sort of like, someone would write a proposal for what we should do next, and then we would argue about like whether it was the right next thing. And, you know, eventually we would pick a thing and then we would go and do that project and we would bring in some contractors and we called it the Hollywood model. We still call it the Hollywood model. Because it was sort of structured like a movie production. We would bring in, you know, to our little core team, we'd bring in a couple specialists, you know, the equivalent of a director of photography or like a, you know, a casting director or whatever, and you bring in the people that you need to accomplish the task. Oh, we don't know how to do Bluetooth on the web. Okay. Find a Bluetooth person. Oh, there's a bunch of crypto stuff, cryptography stuff. Just be clear on this upcoming project, we better find somebody who knows, you know, the ins and outs of like, which cryptography algorithms to use or [00:09:35] what, how to build stuff in C Sharp for Windows platform or Surface, whatever the, the project was over time. You know, we got pretty good at that and I think one of the biggest changes, sort of after we kind of figured out how to actually do work was the realization that. Writing about the work not only gave us a lot of leverage in terms of our sort of visibility in the community and our ability to attract talent, but also the more we put into the writing, the more we learned about the research and that the process of, you know, we would do something and then write a little internal report and then move on. But the process of taking the work that we do, And making it legibl

A conversation with Tim Hwang about historical simulations, the interaction of policy and science, analogies between research ecosystems and the economy, and so much more.  Topics Historical Simulations Macroscience Macro-metrics for science Long science The interaction between science and policy Creative destruction in research "Regulation" for scientific markets Indicators for the health of a field or science as a whole "Metabolism of Science" Science rotation programs Clock speeds of Regulation vs Clock Speeds of Technology References Macroscience Substack Ada Palmer's Papal Simulation Think Tank Tycoon Universal Paperclips (Paperclip maximizer html game) Pitt Rivers Museum   Transcript [00:02:02] Ben: Wait, so tell me more about the historical LARP that you're doing. Oh, [00:02:07] Tim: yeah. So this comes from like something I've been thinking about for a really long time, which is You know in high school, I did model UN and model Congress, and you know, I really I actually, this is still on my to do list is to like look into the back history of like what it was in American history, where we're like, this is going to become an extracurricular, we're going to model the UN, like it has all the vibe of like, after World War II, the UN is a new thing, we got to teach kids about international institutions. Anyways, like, it started as a joke where I was telling my [00:02:35] friend, like, we should have, like, model administrative agency. You know, you should, like, kids should do, like, model EPA. Like, we're gonna do a rulemaking. Kids need to submit. And, like, you know, there'll be Chevron deference and you can challenge the rule. And, like, to do that whole thing. Anyways, it kind of led me down this idea that, like, our, our notion of simulation, particularly for institutions, is, like, Interestingly narrow, right? And particularly when it comes to historical simulation, where like, well we have civil war reenactors, they're kind of like a weird dying breed, but they're there, right? But we don't have like other types of historical reenactments, but like, it might be really valuable and interesting to create communities around that. And so like I was saying before we started recording, is I really want to do one that's a simulation of the Cuban Missile Crisis. But like a serious, like you would like a historical reenactment, right? Yeah. Yeah. It's like everybody would really know their characters. You know, if you're McNamara, you really know what your motivations are and your background. And literally a dream would be a weekend simulation where you have three teams. One would be the Kennedy administration. The other would be, you know, Khrushchev [00:03:35] and the Presidium. And the final one would be the, the Cuban government. Yeah. And to really just blow by blow, simulate that entire thing. You know, the players would attempt to not blow up the world, would be the idea. [00:03:46] Ben: I guess that's actually the thing to poke, in contrast to Civil War reenactment. Sure, like you know how [00:03:51] Tim: that's gonna end. Right, [00:03:52] Ben: and it, I think it, that's the difference maybe between, in my head, a simulation and a reenactment, where I could imagine a simulation going [00:04:01] Tim: differently. Sure, right. [00:04:03] Ben: Right, and, and maybe like, is the goal to make sure the same thing happened that did happen, or is the goal to like, act? faithfully to [00:04:14] Tim: the character as possible. Yeah, I think that's right, and I think both are interesting and valuable, right? But I think one of the things I'm really interested in is, you know, I want to simulate all the characters, but like, I think one of the most interesting things reading, like, the historical record is just, like, operating under deep uncertainty about what's even going on, right? Like, for a period of time, the American [00:04:35] government is not even sure what's going on in Cuba, and, like, you know, this whole question of, like, well, do we preemptively bomb Cuba? Do we, we don't even know if the, like, the warheads on the island are active. And I think I would want to create, like, similar uncertainty, because I think that's where, like, that's where the strategic vision comes in, right? That, like, you have the full pressure of, like, Maybe there's bombs on the island. Maybe there's not even bombs on the island, right? And kind of like creating that dynamic. And so I think simulation is where there's a lot, but I think Even reenactment for some of these things is sort of interesting. Like, that we talk a lot about, like, oh, the Cuban Missile Crisis. Or like, the other joke I had was like, we should do the Manhattan Project, but the Manhattan Project as, like, historical reenactment, right? And it's kind of like, you know, we have these, like, very, like off the cuff or kind of, like, stereotype visions of how these historical events occur. And they're very stylized. Yeah, exactly, right. And so the benefit of a reenactment that is really in detail Yeah. is like, oh yeah, there's this one weird moment. You know, like that, that ends up being really revealing historical examples. And so even if [00:05:35] you can't change the outcome, I think there's also a lot of value in just doing the exercise. Yeah. Yeah. The, the thought of [00:05:40] Ben: in order to drive towards this outcome that I know. Actually happened I wouldn't as the character have needed to do X. That's right That's like weird nuanced unintuitive thing, [00:05:50] Tim: right? Right and there's something I think about even building into the game Right, which is at the very beginning the Russians team can make the decision on whether or not they've even actually deployed weapons into the cube at all, yeah, right and so like I love that kind of outcome right which is basically like And I think that's great because like, a lot of this happens on the background of like, we know the history. Yeah. Right? And so I think like, having the team, the US team put under some pressure of uncertainty. Yeah. About like, oh yeah, they could have made the decision at the very beginning of this game that this is all a bluff. Doesn't mean anything. Like it's potentially really interesting and powerful, so. [00:06:22] Ben: One precedent I know for this completely different historical era, but there's a historian, Ada Palmer, who runs [00:06:30] Tim: a simulation of a people election in her class every year. That's so good. [00:06:35] And [00:06:36] Ben: it's, there, you know, like, it is not a simulation. [00:06:40] Tim: Or, [00:06:41] Ben: sorry, excuse me, it is not a reenactment. In the sense that the outcome is indeterminate. [00:06:47] Tim: Like, the students [00:06:48] Ben: can determine the outcome. But... What tends to happen is like structural factors emerge in the sense that there's always a war. Huh. The question is who's on which sides of the war? Right, right. And what do the outcomes of the war actually entail? That's right. Who [00:07:05] Tim: dies? Yeah, yeah. And I [00:07:07] Ben: find that that's it's sort of Gets at the heart of the, the great [00:07:12] Tim: man theory versus the structural forces theory. That's right. Yeah. Like how much can these like structural forces actually be changed? Yeah. And I think that's one of the most interesting parts of the design that I'm thinking about right now is kind of like, what are the things that you want to randomize to impose different types of like structural factors that could have been in that event? Right? Yeah. So like one of the really big parts of the debate at XCOM in the [00:07:35] early phases of the Cuban Missile Crisis is You know, McNamara, who's like, right, he runs the Department of Defense at the time. His point is basically like, look, whether or not you have bombs in Cuba or you have bombs like in Russia, the situation has not changed from a military standpoint. Like you can fire an ICBM. It has exactly the same implications for the U. S. And so his, his basically his argument in the opening phases of the Cuban Missile Crisis is. Yeah. Which is actually pretty interesting, right? Because that's true. But like, Kennedy can't just go to the American people and say, well, we've already had missiles pointed at us. Some more missiles off, you know, the coast of Florida is not going to make a difference. Yeah. And so like that deep politics, and particularly the politics of the Kennedy administration being seen as like weak on communism. Yeah. Is like a huge pressure on all the activity that's going on. And so it's almost kind of interesting thinking about the Cuban Missile Crisis, not as like You know us about to blow up the world because of a truly strategic situation but more because of like the local politics make it so difficult to create like You know situations where both sides can back down [00:08:35] successfully. Basically. Yeah [00:08:36] Ben: The the one other thing that my mind goes to actually to your point about it model UN in schools. Huh, right is Okay, what if? You use this as a pilot, and then you get people to do these [00:08:49] Tim: simulations at [00:08:50] Ben: scale. Huh. And that's actually how we start doing historical counterfactuals. Huh. Where you look at, okay, you know, a thousand schools all did a simulation of the Cuban Missile Crisis. In those, you know, 700 of them blew [00:09:05] Tim: up the world. Right, right. [00:09:07] Ben: And it's, it actually, I think it's, That's the closest [00:09:10] Tim: thing you can get to like running the tape again. Yeah. I think that's right. And yeah, so I think it's, I think it's a really underused medium in a lot of ways. And I think particularly as like you know, we just talk, talk like pedagogically, like it's interesting that like, it seems to me that there was a moment in American pedagogical history where like, this is a good way of teaching kids. Like, different types of institutions. And like, but

Nadia Asparouhova talks about idea machines on idea machines! Idea machines, of course, being her framework around societal organisms that turn ideas into outcomes. We also talk about  the relationship between philanthropy and status, public goods and more.  Nadia is a hard-to-categorize doer of many things: In the past, she spent many years exploring the funding, governance, and social dynamics of open source software, both writing a book about it called "Working in Public" and putting those ideas into practice at GitHub, where she worked to improve the developer experience. She explored parasocial communities and reputation-based economies as an independent researcher at Protocol Labs and put those ideas into practice as employee number two at Substack, focusing on the writer experience. She's currently researching what the new tech elite will look like, which forms the base of a lot of our conversation.  Completely independently, the two of us came up with the term "idea machines" to describe same thing — in her words: "self-sustaining organisms that contains all the parts needed to turn ideas into outcomes." I hope you enjoy my conversation with Nadia Asparouhova.  Links Nadia's Idea Machines Piece Nadia's Website Working in Public: The Making and Maintenance of Open Source Software Transcript [00:01:59] Ben: I really like your way of, of defining things and sort of bringing clarity to a lot of these very fuzzy words that get thrown around. So, so I'd love to sort of just get your take on how we should think about so a few definitions to start off with. So I, in your mind, what, what is tech, when we talk about like tech and philanthropy what, what is that, what is that entity. [00:02:23] Nadia: Yeah, tech is definitely a fuzzy term. I think it's best to find as a culture, more than a business industry. And I think, yeah, I mean, tech has been [00:02:35] associated with startups historically, but But like, I think it's transitioning from being this like pure software industry to being more like, more like a, a way of thinking. But personally, I don't think I've come across a good definition for tech anywhere. It's kind, you know? [00:02:52] Ben: Yeah. Do, do you think you could point to some like very sort of like characteristic mindsets of tech that you think really sort of set it. [00:03:06] Nadia: Yeah. I think the probably best known would be, you know, failing fast and moving fast and breaking things. I think like the interest in the sort of like David and gly model of an individual that is going up against an institution or some sort of. Complex bureaucracy that needs to be broken apart. Like the notion of disrupting, I think, is a very tech sort of mindset of looking at a problem and saying like, how can we do this better? So it, in a [00:03:35] weird way, tech is, I feel like it's sort of like, especially in relation, in contrast to crypto, I feel like it's often about iterating upon the way things are or improving things, even though I don't know that tech would like to be defined that way necessarily, but when I, yeah. Sort of compare it to like the crypto mindset, I feel like tech is kind of more about breaking apart institutions or, or doing yeah. Trying to do things better. [00:04:00] Ben: A a as opposed. So, so could you then dig into the, the crypto mindset by, by contrast? That's a, I think that's a, a subtle difference that a lot of people don't go into. [00:04:10] Nadia: Yeah. Like I think the crypto mindset is a little bit more about building a parallel universe entirely. It's about, I mean, well, one, I don't see the same drive towards creating monopolies in the way that and I don't know if that was like always a, you know, core value of tech, but I think in practice, that's kind of what it's been of. You try to be like the one thing that is like dominating a market. Whereas with crypto, I think people are [00:04:35] because they have sort of like decentralization as a core value, at least at this stage of their maturity. It's more about building lots of different experiments or trying lots of different things and enabling people to sort of like have their own little corner of the universe where they can, they have all the tools that they need to sort of like build their own world. Whereas the tech mindset seems to imply that there is only one world the world is sort of like dominated by these legacy institutions and it's Tech's job to fix. Those problems. So it's like very much engaged with what it sees as kind of like that, that legacy world or [00:05:10] Ben: Yeah, I, I hadn't really thought about it that way. But that, that totally makes sense. And I'm sure other people have, have talked about this, but do, do you feel that is an artifact of sort of the nature of the, the technology that they're predicated on? Like the difference between, I guess sort of. The internet and the, the internet of, of like SAS and servers and then the [00:05:35] internet of like blockchains and distributed things. [00:05:38] Nadia: I mean, it's weird. Cause if you think about sort of like early computing days, I don't really get that feeling at all. I'm not a computer historian or a technology historian, so I'm sure someone else has a much more nuanced answer to this than I do, but yeah. I mean, like when I think of like sixties, computer or whatever, it, it feels really intertwined with like creating new worlds. And that's why like, I mean, because crypto is so new, it's maybe. It, we can only really observe what's happening right now. I don't know that crypto will always look exactly like this in the future. In fact, it almost certainly will not. So it's hard to know like, what are, it's like core distinct values, but I, I just sort of noticed the contrast right now, at least, but probably, yeah, if you picked a different point in, in text history, sort of like pre startups, I guess and, and pre, or like that commercialization phase or that wealth accumulation phase it was also much more, I guess, like pie this guy. Right. But yeah, it feel, it feels like at least the startup mindset, or like whenever that point of [00:06:35] history started all this sort of like big successes were really about like overturning legacy industries, the, yeah. The term disruption was like such a buzzword. It's about, yeah. Taking something that's not working and making it better, which I think is like very intertwined with like programmer mindset. [00:06:51] Ben: It's yeah, it's true. And I'm just thinking about sort of like my impression of, of the early internet and it, and it did not have that same flavor. So, so perhaps it's a. Artifact of like the stage of a culture or ecosystem then like the technology underlying it. I guess [00:07:10] Nadia: And it's strange. Cause I, I feel like, I mean, there are people today who still sort of maybe fetishizes too strong, a word, but just like embracing that sort of early computing mindset. But it almost feels like a subculture now or something. It doesn't feel. yeah. I don't know. I don't, I don't find that that's like sort of the prevalent mindset in, in tech. [00:07:33] Ben: Well, it, it feels like the, the sort of [00:07:35] like mechanisms that drive tech really do sort of center. I mean, this is my bias, but like, I feel like the, the way that that tech is funded is primarily through venture capital, which only works if you're shooting for a truly massive Result and the way that you get a truly massive result is not to build like a little niche thing, but to try to take over an industry. [00:08:03] Nadia: It's about arbitrage [00:08:05] Ben: yeah. Or, or like, or even not even quite arbitrage, but just like the, the, to like, that's, that's where the massive amount of money is. And, and like, [00:08:14] Nadia: This means her like financially. I feel like when I think about the way that venture capital works, it's it's. [00:08:19] Ben: yeah, [00:08:20] Nadia: ex sort of exploiting, I guess, the, the low margin like cost models. [00:08:25] Ben: yeah, yeah, definitely. And like then using that to like, take over an industry, whereas if maybe like, you're, you're not being funded in a way [00:08:35] that demands, that sort of returns you don't need to take as, as much of a, like take over the world mindset. [00:08:41] Nadia: Yeah. Although I don't think like those two things have to be at odds with each other. I think it's just like, you know, there's like the R and D phase that is much more academic in nature and much more exploratory and then venture capital is better suited for the point in which some of those ideas can be commercialized or have a commercial opportunity. But I don't think, yeah, I don't, I don't think they're like fighting with each other either. [00:09:07] Ben: Really? I, I guess I, I don't know. It's like, so can I, can I, can I disagree and, and sort of say, like, it feels like the, the, the stance that venture type funding comes with, like forces on people is a stance of like, we are, we might fail, but we're, we're setting out to capture a huge, huge amount of value and like, [00:09:35] And, and, and just like in order for venture portfolios to work, that needs to be the mindset. And like there, there are other, I mean, there are just like other funding, ways of funding, things that sort of like ask for more modest returns. And they can't, I mean, they can't take as many risks. They come with other constraints, but, but like the, the need for those, those power law returns does drive a, the need to be like very ambitious in terms of scale. [00:10:10] Nadia: I guess, like what's an example of something that has modest financial returns, but massive social impact that can't be funded through philanthropy and academia or through through venture capital [00:10:29] Ben: Well, I mean, like are, I mean, like, I think that there's, [00:10:35] I think that, that, that, [00:10:38] Nadia: or I guess it [00:10:39] Ben: yeah, I think the philanthropy piece is reall

Seemay Chou talks about the process of building a new research organization, ticks, hiring and managing entrepreneurial scientists, non-model organisms, institutional experiments and a lot more! Seemay is the co-founder and CEO of Arcadia Science —  a research and development company focusing on underesearched areas in biology and specifically new organisms that haven't been traditionally studied in the lab.  She's also the co-founder of Trove Biolabs — a startup focused on harnessing molecules in tick saliva for skin therapies and was previously an assistant professor at UCSF.  She has thought deeply not just about scientific problems themselves, but the meta questions of how we can build better processes and institutions for discovery and invention. I hope you enjoy my conversation with Seemay Chou   Links Seemay on Twitter (@seemaychou) Arcadia's Research Trove Biolabs Seemay's essay about building Arcadia  Transcript [00:02:02] Ben: So since a lot of our conversation is going to be about it how do you describe Arcadia to a smart well-read person who has never actually heard of it before? [00:02:12] Seemay: Okay. I, I actually don't have a singular answer to this smart and educated in what realm. [00:02:19] Ben: oh, good question. Let's assume they have taken some undergraduate science classes, but perhaps are not deeply enmeshed in, in academia. So, so like, [00:02:31] Seemay: enmeshed in the meta science community.[00:02:35]  [00:02:35] Ben: No, no, no, no, but they've, they, they, they, they they're aware that it's a thing, but [00:02:40] Seemay: Yeah. Okay. So for that person, I would say we're a research and development company that is interested in thinking about how we explore under researched areas in biology, new organisms that haven't been traditionally studied in the lab. And we're thinking from first principal polls about all the different ways we can structure the organization around this to also yield outcomes around innovation and commercialization. [00:03:07] Ben: Nice. And how would you describe it to someone who is enmeshed in the, the meta science community? [00:03:13] Seemay: In the meta science community, I would, I would say Arcadias are meta science experiment on how we enable more science in the realm of discovery, exploration and innovation. And it's, you know, that that's where I would start. And then there's so much more that we could click into on that. Right. [00:03:31] Ben: And we will, we will absolutely do that. But before we get there I'm actually really [00:03:35] interested in, in Arcadia's backstory. Cuz cuz when we met, I feel like you were already , well down the, the path of spinning it up. So what's, there's, there's always a good story there. What made you wanna go do this crazy thing? [00:03:47] Seemay: So, so the backstory of Arcadia is actually trove. Soro was my first startup that I spun out together with my co-founder of Kira post. started from a point of frustration around a set of scientific questions that I found challenging to answer in my own lab in academia. So we were very interested in my lab in thinking about all the different molecules and tick saliva that manipulate the skin barrier when a tick is feeding, but basically the, the ideal form of a team around this was, you know, like a very collaborative, highly skilled team that was, you know, strike team for like biochemical, fractionation, math spec, developing itch assays to get this done. It was [00:04:35] not a PhD style project of like one person sort of open-endedly exploring a question. So I was struggling to figure out how to get funding for this, but that wasn't even the right question because even with the right money, like it's still very challenging to set up the right team for this in academia. And so it was during this frustration that I started exploring with Kira about like, what is even the right way to solve this problem, because it's not gonna be through writing more grants. There's a much bigger problem here. Right? And so we started actually talking to people outside of academia. Like here's what we're trying to achieve. And actually the outcome we're really excited about is whether it could yield information that could be acted on for an actually commercializable product, right. There's like skin diseases galore that this could potentially be helpful for. So I think that transition was really important because it went from sort of like a passive idea to, oh, wait, how do we act as agents to figure out how to set this up correctly? [00:05:35] We started talking to angel investors, VCs people in industry. And that's how we learned that, you know, like itch is a huge area. That's an unmet need. And we had tools at our disposal to potentially explore that. So that's how tr started. And that I think was. The beginning of the end or the, the start of the beginning. However you wanna think about it. Because what it did, was it the process of starting trove? It was so fun and it was not at all in conflict with the way I was thinking about my science, the science that was happening on the team was extremely rigorous. And I experienced like a different structure. And that was like the light bulb in my head that not all science should be structured the same way. It really depends on what you're trying to achieve. And then I went down this rabbit hole of trying to study the history of what you might call meta science. Like what are the different structures and iterations of this that have happened over, over the history of even the United States. And it's, hasn't always been the same. Right? And then I think [00:06:35] like, as a scientist, like once you grapple with that, that the way things are now is not how they always have been. Suddenly you have an experiment in front of you. And so that is how Arcadia became born, because I realize. Couched within this trove experiment is so many things that I've been frustrated about that I, I, I don't feel like I've been maximized as the type of scientist that I am. And I really want to think in my career now about not how I fit into the current infrastructure, but like what other infrastructures are available to us. Right? [00:07:08] Ben: Nice. [00:07:09] Seemay: Yeah. So that, that was the beginning. [00:07:11] Ben: and, and so you, you then, I, I, I'm just gonna extrapolate one more, more step. And so you sort of like looked at the, the real, the type of work that you really wanted to do and determined that, that the, the structure of Arcadia that you've built is, is like perhaps the right way to go about enabling that. [00:07:30] Seemay: Okay. So a couple things I, I don't even know yet if Arcadia is the right way to do it. So I [00:07:35] feel like it's important for me to start this conversation there that I actually don't know. But also, yeah, it's a hypothesis and I would also say that, like, that is a beautiful summary, but it's still, it was still a little clunkier than the way you described it and the way I described it. So there's this gap there then of like, okay, what is the optimal place for me to do my science? How do we experiment with this? And I was still acting in a pretty passive way. You know, I was around people in the bay area thinking about like new orgs. And I had heard about this from like ju and Patrick Collison and others, like people very interested in funding and experimenting with new structures. So I thought, oh, if I could find someone else to create an organization. That I could maybe like help advise them on and be a part of, and, and so I started writing up this proposal that I was trying to actually pitch to other people like, oh, would you be interested in leading something like this? [00:08:35] Like, and the more that went on and I, I had like lots and lots and lots of conversations with other scientists in academia, trying to find who would lead this, that it took probably about six months for me to realize like, oh, in the process of doing this, I'm actually leading this. I think and like trying to find someone to hand the keys over to when actually, like, I seem to be the most invested so far. And so I wrote up this whole proposal trying to find someone to lead it and. It came down to that like, oh, I've already done this legwork. Like maybe I should consider myself leading it. And I've, I've definitely asked myself a bunch of times, like, was that like some weird internalized sexism on my part? Cause I was like looking for like someone, some other dude or something to like actually be in charge here. So that's actually how it started. And, and I think a couple people started suggesting to this to me, like if you feel so strongly about this, why aren't you doing this? And I know [00:09:35] it's always an important question for a founder to ask themselves. [00:09:38] Ben: Yeah, yeah, no, that's, that's really clutch. I appreciate you sort of going into the, the, the, the, the, the, like, not straight paths of it. Because, because I guess when we, we put these things into stories, we always like to, to make it like nice and, and linear and like, okay, then this happened and this happened, and here we are. But in reality, it was it's, it's always that ambiguity. Can, can I actually ask two, two questions based on, on that story? One is you, you mentioned that. In academia, even if you had the money, you wouldn't be able to put together that strike team that you thought was necessary. Like why can, can you, can you unpack that a little bit? [00:10:22] Seemay: Yeah. I mean, I think there's a lot of reasons why one of the important reasons, which is absolutely not a criticism of academia, in fact, it's maybe like my support of the [00:10:35] mission in academia is around training and education. That like part of our job as PIs and the research projects we set up is to provide an opportunity for a scientist to learn how to ask questions. How to answer those, how to go through the whole scientific process. And tha

William Bonvillian does a deep dive about his decades of research on how DARPA works and his more recent work on advanced manufacturing.  William is a Lecturer at MIT and the Senior Director of Special Projects,at MIT's Office of Digital Learning. Before joining MIT he spent almost two decades as a senior policy advisor for the US senate. He's also published many papers and a detailed book exploring the DARPA model.  Links William's Website The DARPA Model for Transformative Technologies Transcript [00:00:35] In this podcast, William Bonvillian, and I do a deep dive about his decades of research about how DARPA works and his more recent work on advanced manufacturing. Well humans, a lecturer at MIT and a senior director of special projects at MIT is office of digital learning. Before joining MIT. He spent almost two decades as a senior policy advisor for the us Senate. He's published many papers and a detailed book exploring the DARPA model. I've wanted [00:01:35] to compare notes with him for years. And it was a pleasure. And an honor to finally catch up with him. Here's my conversation with William [00:01:42] Ben: The place that I I'd love to start off is how did you get interested in, in DARPA and the DARPA model in the first place you've been writing about it for more than a decade now. And, and you're probably one of the, the foremost people who who've explored it. So how'd you get there in the first. [00:01:58] William: You know, I, I I worked for the us Senate as a advisor in the Senate for for about 15 years before coming to MIT then. And I I worked for a us Senator who is on the on the armed services committee. And so I began doing a substantial amount of that staffing, given my interest in science technology, R and D and you know, got early contact with DARPA with some of DARPA's both program managers and the DARPA directors, and kind of got to know the agency that way spent some time with them over in their [00:02:35] offices. You know, really kind of got to know the program and began to realize what a, what a dynamic force it was. And, you know, we're talking 20, 20 plus years ago when frankly DARPA was a lot less known than it is now. So yeah, just like you know, kind of suddenly finding this, this Jewelbox varied in the. It was it was a real discovery for me and I became very, very interested in the, kind of the model they had, which was so different than the other federal R and D agencies. [00:03:05] Ben: Yeah. And, and actually um, It sort of in your mind, what is the for, for people who I, I think tend to see different federal agencies that give money to researchers as, as all being in the same bucket. What, what do you, what would you describe the difference between DARPA and the NSF as being [00:03:24] William: well? I mean, there's a big difference. So the NSF model is to support basic research. And they have, you know, the equivalent of project [00:03:35] managers there and they, they don't do the selecting of the research projects. Instead they queue up applicants for funds and then they supervise a peer review process. Of experts, you know, largely from academia who evaluate, you know, a host of proposals in a, in a given R and D area mm-hmm and and make valuations as to which ones would qualify. What are the kind of best most competitive applicants for NSFs basic research. So DARPA's got a different project going on, so it doesn't work from the bottom up. It, it has strong program managers who are in effect kind of empowered to go out and create new things. So they're not just, you know, responding to. Grant applications for basic research, they come into DARPA and develop a [00:04:35] vision of a new breakthrough technology area. They wanna stand up. And so it's, and there's no peer review here. It's really, you hire talented program managers. And you unleash them, you turn them loose, you empower them to go out and find the best work that's going on in the country. And that's, that can be from, from universities and often ends in this breakthrough technology area they've identified. But it also could be from comp companies, often smaller companies and typically they'll construct kind of a hybrid model where they've got academics. Companies working on a project, the companies are already always oriented to getting the technology out the door. Right. Cause they have to survive, but the researchers are often in touch with some of the more breakthrough capabilities behind the research. So bringing those two together is something that the program manager at DARPA does. So while at [00:05:35] NSF, the program manager equivalent, you know, their big job is getting grant out the door and supervising a complex selection process by committee mm-hmm . The role of the, of the ARPA of the, of the DARPA program manager is selecting the award winners is just the beginning of the job. Then in effect you move into their home, right? You work with them on an ongoing basis. DARPA program managers are spending at least one third of their time on the road, linking up with their, you know, with their grantees, the folks they've contracted with sort of helping them along in the process. And then, you know, the typically fund a group of research awards in an area they'll also work on putting together kind of a thinking community amongst those award winners. Contract winners so that they begin to share their best ideas. And that's not a, that's not easy, right? Yeah. Yeah. If you're an academic [00:06:35] or you, a company, you stuff, you trading ideas is a complicated process, but that's one of the tasks. That the DARPA program manager has, is to really build these thinking communities around problems. And that's what they that's what they're driven to do. So it's a very, very different situation. This is, this is the different world here that Dar is created [00:07:01] Ben: and, and sort of actually to, to, to click on The, the how DARPA program managers interact with ideas. Do you have a sense of how they incentivize that idea sharing? Is it just the, the concept that if you share these ideas, they might get funded in a way that they wouldn't or like what, how do they sort of construct that That trust that people for people could actually be sharing those ideas. [00:07:28] William: Yeah. In, in some ways then it starts out at an all stage. So before, you know, a new [00:07:35] program manager arrives at DARPA and often they'll have, I mean, this could be ape. It could be I RPA, which worked slightly different ways, but similar kind of approach RPE is our energy DARPA. I, APA is our intelligence Dar. Right. And then soon we'll have a help DARPA, which has now been funded. Yeah. I wanna [00:07:55] Ben: your opinion on that later. [00:07:57] William: Okay. Well, we're working away on this model here. You know, you hire a program manager and you hire somebody. Who's gonna be, you know, talent and dynamic and kind of entrepreneurial and standing up a new program. They get the DARPA and they begin to work on this new technology area. And a requirement of DARPA is that really be a breakthrough. They don't wanna fund incremental work that somebody else may be doing. They wanna find a new, new territory. That's their job, revolutionary breakthroughs. To get there. They'll often convene workshops, 1, 2, 3 workshops with some of the best thinkers around the country, including people, [00:08:35] people who may be applying for the funding, but they'll, they'll look for the best people bringing together and get, you know, a day long process going um, often in several different locations to kind of think through. Technology advance opportunity. How, how it might shape up what might contribute, how might you organize it? What research might go into it, what research areas and that kind of begins the kind of thinking process of building a community around a problem. And then they'll make grant awards. And then similarly, they're gonna be frequently convening this group and everybody can sit on their hands and keep their mouth shut. But you know, that's not often the way technologists work. They'll get into a problem and start wanting to share ideas and brainstorm. And that's, that's typically what then takes place and part of the job of the, of. Partner manager DARPA is to really encourage that kind of dialogue and get a lot of ideas on the table and really promote it. Yeah. [00:09:34] Ben: [00:09:35] And, and then also with, with those ideas do, do you have, like, in your, your having looked at this so much, do you have a sense of how much there there's this tension? You know, it's like people generally do the best research when they feel a lot of ownership over their own ideas and they feel like they're, they're really working on. The, the thing that they want to work on. But then at the same time to sort of for, for, for the, a project to play into a broader program, you often need to sort of adjust ideas towards sort of a, a bigger system or a bigger goal. Do you have, do you have an idea of how much Program managers sort of shape what people are working on versus just sort of enabling people to work on things that they would want to work on. Otherwise. [00:10:24] William: Yeah. The program manager in communication with DARPA's office directors and director. Right, right. So it's a very flat organization. You know, and [00:10:35] there'll be an office director and a number of program managers working with that office director. For example in the field of, of biological technologies, a fairly new DARPA office set up about a decade ago. Yeah. You know, there'll be a group of DARPA program managers with expertise in that field and they will often have often a combination of experiences. They'll have some company experience as well as some academic research experience that they're kind of walking on both sides. They'll come into DARPA often with some ideas about things they want to pursue, right. And then they'll start th

In this conversation, Adam Falk and I talk about running research programs with impact over long timescales, creating new fields, philanthropic science funding, and so much more.  Adam is the president of the Alfred P. Sloan Foundation,  which was started by the eponymous founder of General Motors and has been funding science and education efforts for almost nine decades.  They've funded everything from iPython Notebooks to the Wikimedia foundation to an astronomical survey of the entire sky. If you're like me, their name is familiar from the acknowledgement part of PBS science shows. Before becoming the president of the Sloan Foundation, Adam was the president of Williams College and a high energy physicist focused on elementary particle physics and quantum field theory. His combined experience in research, academic administration, and philanthropic funding give him a unique and fascinating perspective on the innovation ecosystem. I hope you enjoy this as much as I did.  Links - The Sloan Foundation - Adam Falk on Wikipedia  - Philanthropy and the Future of Science and Technology Highlight Timestamps - How do you measure success in science? [00:01:31] - Thinking about programs on long timescales [00:05:27] -  How does the Sloan Foundation decide which programs to do? [00:08:08] - Sloan's Matter to Life Program [00:12:54] -  How does the Sloan Foundation think about coordination? [00:18:24] -  Finding and incentivizing program directors [00:22:32] - What should academics know about the funding world and what should the funding world know about academics? [00:28:03] - Grants and academics as the primary way research happens [00:33:42] - Problems with grants and common grant applications [00:44:49] - Addressing the criticism of philanthropy being inefficient because it lacks market mechanisms [00:47:16] - Engaging with the idea that people who create value should be able to capture that value [00:53:05]   Transcript [00:00:35] In this conversation, Adam Falk, and I talk about running research programs with impact over long timescales, creating new fields, philanthropic science funding, and so much more. Adam is the president of the Alfred P Sloan foundation, which was started by the eponymous founder of general motors. And has been funding science and education efforts for almost nine decades. They funded everything from IP. I fond [00:01:35] notebooks to Wikimedia foundation. To an astronomical survey of the entire sky. If you're like me, their name is familiar from the acknowledgement part of PBS science shows. Before becoming the president of the Sloan foundation. Adam was the president of Williams college and I high energy physicist focused on elementary particle physics in quantum field theory. His combined experience in research. Uh, Academic administration and philanthropic funding give him a unique and fascinating perspective on the innovation ecosystem i hope you enjoy this as much as i did [00:02:06] Ben: Let's start with like a, sort of a really tricky thing that I'm, I'm myself always thinking about is that, you know, it's really hard to like measure success in science, right? Like you, you know, this better than anybody. And so just like at, at the foundation, how do you, how do you think about success? Like, what is, what does success look like? What is the difference between. Success and failure mean to [00:02:34] Adam: you? [00:02:35] I mean, I think that's a, that's a really good question. And I think it's a mistake to think that there are some magic metrics that if only you are clever enough to come up with build them out of citations and publications you could get some fine tune measure of success. I mean, obviously if we fund in a scientific area, we're funding investigators who we think are going to have a real impact with their work individually, and then collectively. And so of course, you know, if they're not publishing, it's a failure. We expect them to publish. We expect people to publish in high-impact journals, but we look for broader measures as well if we fund a new area. So for example, A number of years ago, we had a program in the microbiology of the built environment, kind of studying all the microbes that live in inside, which turns out to be a very different ecosystem than outside. When we started in that program, there were a few investigators interested in this question. There weren't a lot of tools that were good for studying it. [00:03:35] By 10 years later, when we'd left, there was a journal, there were conferences, there was a community of people who were doing this work, and that was another measure, really tangible measure of success that we kind of entered a field that, that needed some support in order to get going. And by the time we got out, it was, it was going strong and the community of people doing that work had an identity and funding paths and a real future. Yeah. [00:04:01] Ben: So I guess one way that I've been thinking about it, it's just, it's almost like counterfactual impact. Right. Whereas like if you hadn't gone in, then it, the, it wouldn't be [00:04:12] Adam: there. Yeah. I think that's the way we think about it. Of course that's a hard to, to measure. Yeah. But I think that Since a lot of the work we fund is not close to technology, right. We don't have available to ourselves, you know, did we spin out products? Did we spin out? Companies did a lot of the things that might directly connect that work to, [00:04:35] to activities that are outside of the research enterprise, that in other fields you can measure impact with. So the impact is pretty internal. That is for the most part, it is, you know, Has it been impact on other parts of science that, you know, again, that we think might not have happened if we hadn't hadn't funded what we funded. As I said before, have communities grown up another interesting measure of impact from our project that we funded for about 25 years now, the Sloan digital sky survey is in papers published in the following sense that one of the innovations, when the Sloan digital sky survey launched in the early. Was that the data that came out of it, which was all for the first time, digital was shared broadly with the community. That is, this was a survey of the night sky that looked at millions of objects. So they're very large databases. And the investigators who built this, the, the built the, the, the telescope certainly had first crack at analyzing that [00:05:35] data. But there was so much richness in the data that the decision was made at. Sloan's urging early on that this data after a year should be made public 90% of the publications that came out of the Sloan digital sky survey have not come from collaborators, but it come from people who use that data after it's been publicly released. Yeah. So that's another way of kind of seeing impact and success of a project. And it's reached beyond its own borders. [00:06:02] Ben: And you mentioned like both. Just like that timescale, right? Like that, that, that 25 years something that I think is just really cool about the Sloan foundation is like how, how long you've been around and sort of like your capability of thinking on those on like a quarter century timescale. And I guess, how do you, how do you think about timescales on things? Right. Because it's like, on the one hand, this is like, obviously like science can take [00:06:35] 25 years on the other hand, you know, it's like, you need to be, you can't just sort of like do nothing for 25 years. [00:06:44] Adam: So if you had told people back in the nineties that the Sloan digital sky survey was going to still be going after a quarter of a century, they probably never would have funded it. So, you know, I think that That you have an advantage in the foundation world, as opposed to the the, the federal funding, which is that you can have some flexibility about the timescales on what you think. And so you don't have to simply go from grant to grant and you're not kind of at the mercy of a Congress that changes its own funding commitments every couple of years. We at the Sloan foundation tend to think that it takes five years at a minimum to have impact into any new field that you go into it. And we enter a new science field, you know, as we just entered, we just started a new program matter to life, which we can talk about. [00:07:35] That's initially a five-year commitment to put about $10 million a year. Into this discipline, understanding that if things are going well, we'll re up for another five years. So we kind of think of that as a decadal program. And I would say the time scale we think on for programs is decades. The timescale we think of for grants is about three years, right? But a program itself consists of many grants may do a large number of investigators. And that's really the timescale where we think you can have, have an impact over that time. But we're constantly re-evaluating. I would say the timescale for rethinking a program is shorter. That's more like five years and we react. So in our ongoing programs, about every five years, we'll take a step back and do a review. You know, whether we're having an impact on the program, we'll get some outside perspectives on it and whether we need to keep it going exactly as it is, or adjust in some [00:08:35] interesting ways or shut it down and move the resources somewhere else. So [00:08:39] Ben: I like that, that you have, you almost have like a hierarchy of timescales, right? Like you have sort of multiple going at once. I think that's, that's like under underappreciated and so w one thing they want to ask about, and maybe the the, the life program is a good sort of like case study in this is like, how do you, how do you decide what pro, like, how do you decide what programs to do, right? Like you could do anything. [00:09:04] Adam: So th that is a terrific question and a hard one to get. Right. And we just came out of a process of thinking very deepl

In this conversation, Semon Rezchikov and I talk about what other disciplines can learn from mathematics, creating and cultivating collaborations, working at different levels of abstraction, and a lot more! Semon is currently a postdoc in mathematics at Harvard where he specializes in symplectic geometry. He has an amazing ability to go up and down the ladder of abstraction — doing extremely hardcore math while at the same time paying attention to *how* he's doing that work and the broader institutional structures that it fits into. Semon is worth listening to both because he has great ideas and also because in many ways, academic mathematics feels like it stands apart from other disciplines. Not just because of the subject matter, but because it has managed to buck many of the trend that other fields experienced over the course of the 20th century.   Links Semon's Website Transcript [00:00:35]  Welcome back to idea machines. Before we get started, I'm going to do two quick pieces of housekeeping. I realized that my updates have been a little bit erratic. My excuse is that I've been working on my own idea machine. That being said, I've gotten enough feedback that people do get something out of the podcast and I have enough fun doing it that I am going to try to commit to a once a month cadence probably releasing on the pressure second [00:01:35] day of. Second thing is that I want to start doing more experiments with the podcast. I don't hear enough experiments in podcasting and I'm in this sort of unique position where I don't really care about revenue or listener numbers. I don't actually look at them. And, and I don't make any revenue. So with that in mind, I, I want to try some stuff. The podcast will continue to be a long form conversation that that won't change. But I do want to figure out if there are ways to. Maybe something like fake commercials for lesser known scientific concepts, micro interviews. If you have ideas, send them to me in an email or on Twitter. So that's, that's the housekeeping. This conversation, Simon Rezchikov and I talk about what other disciplines can learn from mathematics, creating and cultivating collaborations, working at different levels of abstraction. is currently a post-doc in mathematics at Harvard, where he specializes in symplectic geometry. He has an amazing ability to go up, go up and down the ladder of [00:02:35] abstraction, doing extremely hardcore math while at the same time, paying attention to how he's doing the work and the broader institutional structures that affect. He's worth listening to both because he has great ideas. And also because in many ways, academic mathematics feels like it stands apart from other disciplines, not just because of the subject matter, but because it has managed to buck many of the trends that other fields experience of the course of the 20th century. So it's worth sort of poking at why that happened and perhaps. How other fields might be able to replicate some of the healthier parts of mathematics. So without further ado, here's our conversation. [00:03:16] Ben:  I want to start with the notion that I think most people have that the way that mathematicians go about a working on things and be thinking about how to work on things like what to work on is that you like go in a room and you maybe read some papers and you think really hard, and then [00:03:35] you find some problem. And then. You like spend some number of years on a Blackboard and then you come up with a solution. But apparently that's not that that's not how it actually works.  [00:03:49] Semon: Okay. I don't think that's a complete description. So definitely people spend time in front of blackboards. I think the length of a typical length of a project can definitely. Vary between disciplines I think yeah, within mathematics. So I think, but also on the other hand, it's also hard to define what is a single project. As you know, a single, there might be kind of a single intellectual art through which several papers are produced, where you don't even quite know the end of the project when you start. But, and so, you know, two, a two years on a single project is probably kind of a significant project for many people. Because that's just a lot of time, but it's true that, you know, even a graduate student might spend several years working on at least a single kind of larger set of ideas because the community does have enough [00:04:35] sort of stability to allow for that. But it's not entirely true that people work alone. I think these days mathematics is pretty collaborative people. Yeah. If you're mad, you know, in the end, you're kind of, you probably are making a lot of stuff up and sort of doing self consistency checks through this sort of formal algebra or this sort of, kind of technique of proof. It makes you make sure helps you stay sane. But when other people kind of can think about the same objects from a different perspective, usually things go faster and at the very least it helps you kind of decide which parts of the mathematical ideas are really. So often, you know, people work with collaborators or there might be a community of people who were kind of talking about some set of ideas and they may be, there may be misunderstanding one another, a little bit. And then they're kind of biting off pieces of a sort of, kind of collective and collectively imagined [00:05:35] mathematical construct to kind of make real on their own or with smaller groups of people. So all of those  [00:05:40] Ben: happen. And how did these collaborations. Like come about and  [00:05:44] Semon: how do you structure them? That's great. That's a great question. So I think this is probably several different models. I can tell you some that I've run across. So during, so sometimes there are conferences and then people might start. So recently I was at a conference and I went out to dinner with a few people, and then after dinner, we were. We were talking about like some of our recent work and trying to understand like where it might go up. And somebody, you know, I was like, oh, you know, I didn't get to ask you any questions. You know, here's something I've always wanted to know from you. And they were like, oh yes, this is how this should work. But here's something I don't know. And then somehow we realized that you know, there was some reasonable kind of very reasonable guests as to what the answer is. Something that needed to be known would be so I guess now we're writing a paper together, [00:06:35] hopefully that guess works. So that's one way to start a collaboration. You go out to a fancy dinner and afterwards you're like, Hey, I guess we maybe solved the problem. There is other ways sometimes people just to two people might realize they're confused about the same thing. So. Collaboration like that kind of from somewhat different types of technical backgrounds, we both realized we're confused about a related set of ideas. We were like, okay, well I guess maybe we can try to get unconfused together.  [00:07:00] Ben: Can I, can I interject, like, I think it's actually realizing that you are confused about the same problem as someone who's coming at it from a different direction is actually hard in and of itself. Yes. Yes. How, how does, like, what is actually the process of realizing that the problem that both of you have is in fact the same problem? Well,  [00:07:28] Semon: you probably have to understand a little bit about the other person's work and you probably have to in some [00:07:35] way, have some basal amount of rapport with the other person first, because. You know, you're not going to get yourself to like, engage with this different foreign language, unless you kind of like liked them to some degree. So that's actually a crucial thing. It's like the personal aspect of it. Then you know it because maybe you'll you kind of like this person's work and maybe you like the way they go about it. That's interesting to you. Then you can try to, you know, talk about what you've recently been thinking about. And then, you know, the same mathematical object might pop up. And then that, that sort of, that might be you know, I'm not any kind of truly any mathematical object worth studying, usually has incarnations and different formal languages, which are related to one another through kind of highly non-obvious transformation. So for example, everyone knows about a circle, but a circle. Could you could think of that as like the set of points of distance one, you could think of it as some sort of close, not right. You can, you can sort of, there are many different concrete [00:08:35] intuitions through which you can grapple with this sort of object. And usually if that's true, that sort of tells you that it's an interesting object. If a mathematical object only exists because of a technicality, it maybe isn't so interesting. So that's why it's maybe possible to notice that the same object occurs in two different peoples. Misunderstandings. [00:08:53] Ben: Yeah. But I think the cruxy thing for me is that it is at the end of the day, it's like a really human process. There's not a way of sort of colliding what both of, you know, without hanging out.  [00:09:11] Semon: So people. And people can try to communicate what they know through texts. So people write reviews on. I gave a few talks recently in a number of people have asked me to write like a review of this subject. There's no subject, just to be clear. I kind of gave a talk with the kind of impression that there is a subject to be worked on, but nobody's really done any work on it that you're [00:09:35] meeting this subject into existence. That's definitely part of your job as an academic. But you know, then that's one way of explaining, I think that, that can be a little bit less, like one-on-one less personal. People also write these a different version of that is people write kind of problems. People write problem statements.

Professor Michael Strevens discusses the line between scientific knowledge and everything else, the contrast between what scientists as people do and the formalized process of science, why Kuhn and Popper are both right and both wrong, and more. Michael is a professor of Philosophy at New York University where he studies the philosophy of science and the philosophical implications of cognitive science. He's the author of the outstanding book "The Knowledge Machine" which is the focus of most of our conversation. Two ideas from the book that we touch on: 1. "The iron rule of science". The iron rule states that "`[The Iron Rule] directs scientists to resolve their differences of opinion by conducting empirical tests rather than by shouting or fighting or philosophizing or moralizing or marrying or calling on a higher power` in the book Michael Makes a strong argument that scientists following the iron rule is what makes science work. 2. "The Tychonic principle." Named after the astronomer Tycho Brahe who was one of the first to realize that very sensitive measurements can unlock new knowledge about the world, this is the idea that the secrets of the universe lie in minute details that can discriminate between two competing theories. The classic example here is the amount of change in star positions during an eclipse dictated whether Einstein or Newton was more correct about the nature of gravity. Links Michael's Website The Knowledge Machine on BetterWorldBooks Michael Strevens talks about The Knowledge Machine on The Night Science Podcast  Michael Strevens talks about The Knowledge Machine on The Jim Rutt Show    Automated Transcript [00:00:35] In this conversation. Uh, Professor Michael And I talk about the line between scientific knowledge and everything else. The contrast between what scientists as people do and the formalized process of science, why Coon and popper are both right, and both wrong and more. Michael is a professor of philosophy at New York university, where he studies the philosophy of science and the philosophical implications [00:01:35] of cognitive science. He's the author of the outstanding book, the knowledge machine, which is the focus of most of our conversation. A quick warning. This is a very Tyler Cowen ESCA episode. In other words, that's the conversation I wanted to have with Michael? Not necessarily the one that you want to hear. That being said I want to briefly introduce two ideas from the book, which we focus on pretty heavily. First it's what Michael calls the iron rule of science. Direct quote from the book dine rule states that the iron rule direct scientists to resolve their differences of opinion by conducting empirical tests, rather than by shouting or fighting or philosophizing or moralizing or marrying or calling on a higher power. In the book, Michael makes a strong argument that scientist's following the iron rule is what makes science work. The other idea from the book is what Michael calls the Taconic principle. Named after the astronomer Tycho Brahe, who is one of the first to realize that very sensitive measurements can unlock new [00:02:35] knowledge about the world. This is the idea that the secrets of the universe that lie into my new details that can discriminate between two competing theories. The classic example, here is the amount of change in a Star's position during an eclipse dictating whether Einstein or Newton was more correct about the nature of gravity. So with that background, here's my conversation with professor Michael strengthens. [00:02:58] Ben: Where did this idea of the, this, the sort of conceptual framework that you came up with come from? Like, what's like almost the story behind the story here. [00:03:10] Michael: Well, there is an interesting origin story, or at least it's interesting in a, in a nerdy kind of way. So it was interested in an actually teaching the, like what philosophers call that logic of confirmation, how, how evidence supports or undermines theories. And I was interested in getting across some ideas from that 1940s and fifties. Scientists philosophers of science these days [00:03:35] look back on it and think of as being a little bit naive and clueless. And I had at some point in trying to make this stuff appealing in the right sort of way to my students so that they would see it it's really worth paying attention. And just not just completely superseded. I had a bit of a gear shift looking at it, and I realized that in some sense, what this old theory was a theory of, wasn't the thing that we were talking about now, but a different thing. So it wasn't so much about how to assess how much a piece of evidence supports a theory or undermines it. But was it more a theory of just what counts as evidence in the first place? And that got me thinking that this question alone is, could be a important one to, to, to think about now, I ended up as you know, in my book, the knowledge machine, I'm putting my finger on that as the most important thing in all of science. And I can't say it at that point, I had yet had that idea, but it was, [00:04:35] it was kind of puzzling me why it would be that there would, there would be this very kind of objective standard for something counting is evidence that nevertheless offered you more or less, no help in deciding what the evidence was actually telling you. Why would, why would this be so important at first? I thought maybe, maybe it was just the sheer objectivity of it. That's important. And I still think there's something to that, but the objectivity alone didn't seem to be doing enough. And then I connected it with this idea in Thomas Kuhn's book, the structure of scientific revolutions that, that science is is a really difficult pursuit that I've heard. And of course it's wonderful some of the time, but a lot of. requires just that kind of perseverance in the face of very discouraging sometimes. Oh, it's I got the idea that this very objective standard for evidence could be playing the same role that Coon Coon thought was played by what he called the paradigm bar, providing a kind of a very objective framework, which is also a kind of a safe framework, [00:05:35] like a game where everyone agrees on the rules and where people could be feeling more comfortable about the validity and importance of what they were doing. Not necessarily because they would be convinced it would lead to the truth, but just because they felt secure in playing a certain kind of game. So it was a long, it was a long process that began with this sort of just something didn't seem right about these. It didn't seem right that these ideas from the 1940s and fifties could be so, so so wrong as answers to the question. Philosophers in my generation, but answering. Yeah, no, it's, [00:06:11] Ben: I love that. I feel in a way you did is like you like step one, sort of synthesized Coon and popper, and then went like one step beyond them. It's, it's this thing where I'm sure you'd go this, this, the concept that whenever you have like two, two theories that seem equally right. But are [00:06:35] contradictory, that demand is like that, that is a place where, you know, you need more theory, right? Because like, you look at popper and it's like, oh yeah, that seems, that seems right. But then there's you look at Kuhn and you're like, oh, that seems right. And then you're like, wait a minute. Because like, they sort of can't both live in the broom without [00:06:56] Michael: adding something. Although there is something there's actually something I think. Pop Harrington about Koons ideas now. And there's lots of things that are very unpopped period, but you know, Papa's basic idea is science proceeds through reputation and Koons picture of science is a little bit like a very large scale version of that, where we're scientists now, unlike in Papa's story by scientists, we're all desperately trying to undermine theories, you know, the great Britain negative spirits. And with, with, they just assume that that prevailing way of doing things, the paradigm is going to work out okay. But in presuming that they push it to its breaking point. And [00:07:35] that process, if you kind of take a few steps back, has the look of pop and science in the sense that, in the sense that scientists, but now unwittingly rather than with their critical faculties, fully engaged and wittingly are, are taking the theory to a point where it just cannot be sustained anymore in the face of the evidence. And it progresses made because the theory just becomes antenna. Some other theory needs to be counted. So there's at, at the largest scale, there's this process of that, of success of reputation and theories. Now, Coon reputation is not quite the right word. That sounds too orderly and logical to capture what it's doing, but it is nevertheless, there is being annihilated by facts and in a way that's actually quite a period. I think that interesting. [00:08:20] Ben: So it's like, like you could almost phrase Coon as like systemic pop area. Isn't right. To like no individual scientist is trying to do reputation, but then you have like the system eventually [00:08:35] refutes. And that like, that is what the paradigm shift [00:08:37] Michael: is. That's exactly right. Oh, [00:08:39] Ben: that's fast. Another thing that I wanted to ask before we dig into the actual meat of the book is like, wow, this is, this is almost a very, very selfish question, but like, why should people care about this? Like, I really care about it. There's some, and by this, I mean like sort of the, like theories of how science works, right? Like, but I know, I know many scientists who don't care. They're just like, I tried to, I talked to them about that because then they're like, like I just, you know, it's like I do, I do. I think, [00:09:12] Michael: you know, in a way that, and that's completely fine, you know, people to drive a car, you don't know how the engine works.

A conversation with the VitaDAO core team. VitaDAO is a decentralized autonomous organization — or DAO — that focuses on enabling and funding longevity research. The sketch of how a DAO works is that people buy voting tokens that live on top of the Etherium blockchain and then use those tokens to vote on various action proposals for VitaDAO to take. This voting-based system contrasts with the more traditional model of a company that is a creation of law or contact, raises capital by selling equity or acquiring debt, and is run by an executive team who are responsible to a board of directors. Since technically nobody runs VitaDAO the way a CEO runs a company, I wanted to try to embrace the distributed nature and talk to many of the core team at once. This was definitely an experiment! The members of the core team in the conversation in no particular order: Tyler Golato Paul Kohlhaas Vincent Weisser Tim Peterson Niklas Rindtorff Laurence Ion Links VitaDAO Home Page An explanation of what a DAO is Molecule Automated Transcript VitaDAO [00:00:35]  In This conversation. I talked to a big chunk of the VitaDAO core team. VitaDAO is a decentralized autonomous organization or Dao that focuses on enabling and funding. Longevity research. We get into the details in the podcast, but a sketch of how a DAO works is that people buy voting tokens that live on top of the Ethereum blockchain.  And then they use those tokens to vote on [00:01:35] various action proposals for me to doubt to take. This voting based system contrasts with more traditional models of the company. That is a creation of law or contract raises capital by selling equity or acquiring debt, and is run by an executive team who are responsible to a board of directors.  Since technically, nobody runs for you to doubt the way it CEO runs the company. I wanted to try to embrace the distributed nature and talk to many of the core team at once. This was definitely experiment. Uh, I think it's your day. Well, Oh, well, but I realize it can be hard to tell voices apart on a podcast.  So I'll put a link to a video version. In the show notes. So without further ado, here's my conversation with Vita Dao.  What I want to do so that listeners can put a voice to a name is I want to go around everybody say your name and then you say how you would pronounce the word VI T a D a O. Tim, would you say your name and then, and then pronounce the word that [00:02:35] that's kind of how I've done it. Yeah. And so I'm the longevity steward we can help kind of figure out deal flow on, edited out, so. Awesome. All right, Tyler, you're next on. It is definitively Vieta Dell. Yeah. And I also help out with the longevity steward group. I started starting longevity group and I'm the chief scientific officer and co-founder at molecule as well. And then Nicholas you're next on my screen. It's definitely beats it out. And I'm also a member of the longevity working group in this science communication group and also currently initiating and laptop. Great. And then Vinson. Yeah. So it's the same pronunciation weeded out, but I'm helping on the side and also on kind of like special projects, like this incline where that I took around, we had recently and yeah, in Lawrence. Lauren Sajjan Vieta thou. And I [00:03:35] also steward the deal flow group within the longevity working group. And I think we should all now say as a hive mind, Paul Paul has said at the same time, oh, sorry. I'm going to say bye to dad. Mess with her in yeah. Hi everyone. My name is Paul cohost. I would say, be to down. I actually wonder what demographics says, Vida, like RESA. We should actually look into that. It's interest, interesting community metric. I'm the CEO and co-founder of molecule and one of the co-authors of the VW. I also work very deeply on the economic side and then essentially help finalize deal structures. So essentially the funding deals that we've been carry through into molecule and yeah, very excited to be here today. And maybe we can jump back into Lawrence adjusted  we well, [00:04:35] also, so the thing that's confusing to me is that I always assumed that the Vith came from the word vitality. Right. And so that's, that's where the idea of calling it a fight Vita doubt, right? Because like, I don't say vitality, I say fighting. In German, it's actually retaliatory. Yeah. So it's just like the stupid Anglo centrism that is from the Latin, I would say from the word life. Yeah. Cool. So to really sort of jump right in, I think there's the, to like, be very direct, like, can we like walk through the mechanics of how the, how, how everything actually works? Right. So I think listeners are probably familiar with sort of like the high level abstract concept of there's a bunch of people. They have tokens, they vote on deals you give researchers money to, to do work, but like, sort of [00:05:35] like very, very mechanical. How does the dowel work? Could you like walk us through maybe like, sort of a a core loop of, of like what, what you do Yeah. So I mean, the core goal of the DAO is really to try and democratize access to decision-making funding and governance of longevity therapeutics. And so mechanically, there's a few different things going on and anyone feel free to interrupt me or jump in as well. But, so I would start from the base layer is really having this broad community of decentralized token holders, which are ultimately providing governance functions to this community. And the community's goal is to deploy funding that it's raised into early stage. Clinical proof of concept stage longevity therapeutics projects. And these basically fall between these two, let's say points where some tension exists in when it comes to translating academic science. So you have this robust early stage, let's say basic research funding mechanism through things like the NIH [00:06:35] grant funding, essentially. And that gets really to the point of being able to do, let's say very early stage drug discovery. And there's also some sort of downstream ecosystem consisting of venture capital company builders, political companies that does let's say late stage funding and incubation of ideas. They're more well-vetted, but between there's this sort of problem where a lot of innovation gets lost, it's known as the translational valley of death. Yeah. What did we try to do is we try to identify as a community academics that are working and let's say, have stumbled onto a potentially promising drug, but aren't really at the point yet where they can create a startup company. And what we want to do is basically by working together as a community, provide them the funding, the resources, in some cases, even the incubation functions to be able to do a series of killer experiments, really deep risk of project, and then file intellectual property, which in exchange for the funding, the dowel actually, and this is, this is sort of mechanically enabled by a legal primitive that we've been developing a molecule called an IP [00:07:35] NFP framework, which basically consists on one side of a legal contract, typically in the form of a sponsored research search agreement between a funder and a party that would be receiving the funding, the laboratory, and on the other side of federated data storage layer. And so the way this works is basically beat a doubt would receive applications. Some of these projects could, for example, be listed on molecules marketplace have an IPN T created meta dealt with would send funds via the system to the university and in exchange, they would hold this license and essence for the IP, that results from that project. And then within the community, we have domain experts. For example, we have a longevity working group which consists of MDs. Post-docs PhD is basically anyone that has deep domain experience in the longevity space. They work to evaluate projects due diligence and ultimately serve as sort of a quality control filter for the community, which consists of non-experts as well. Maybe just people who are enthusiastic about what. And beyond that, there's also additional domain expertise in the [00:08:35] form of some people who have worked at biotech VCs, for example, people with entrepreneurial experience and through this community, you basically try to form, let's say a broad range of expertise that can then coach the research or work with them and really help the academic move the IP and the research project towards the stage where, where it can be commercialized. And now VitaDAO stewarding this process. They have ownership in the IP and basically what would happen is if that research has out license co-developed sold onto another party, just made productive in essence and. It's successful in commercializing those efforts and received some funds, let's say from the commercialization of that asset, that goes back into the treasury and is continuously deployed into longevity research. So the long-term goal is to really create this sort of self-sustaining circular funding mechanism to continue to fund longevity research over time. And now within that, we could wrap it all into, you know, there's a bunch of like specific mechanics in there. I would love to, to rabbit hole, [00:09:35] I think Vincent, yes, to and on the kind of very simple technical layer, kind of very initially we started off just having this idea and putting it out there and then like having like a kind of Genesis auction where everyone could contribute funds. Like some people contribute 200 bucks and others contributed millions. And in exchange for that. Just like as a, there is an example, like for every dollar they gave, they gave, got one vote in organization. And then this initial group of people that came together to put, to, to pool their resources, to fund longevity, research, got votes and exchange, and actually with these votes, basically they can then what Tyler described make on the, on these proposals that that ar

Dr. Brian Arthur and I talk about how technology can be modeled as a modular and evolving system, combinatorial evolution more broadly and dig into some fascinating technological case studies that informed his book The Nature of Technology. Brian is a researcher and author who is perhaps best known for his work on complexity economics, but I wanted to talk to him because of the fascinating work he's done building out theories of technology. As we discuss, there's been a lot of theorizing around science — with the works of Popper, Kuhn and others. But there's been less rigorous work on how technology works despite its effects on our lives. Brian currently works at PARC (formerly Xerox PARC, the birthplace of personal computing) and has also worked at the Santa Fe institute and was a professor Stanford university before that. Links W. Brian Arthur's Wikipedia Page The Nature of Technology on Amazon W. Brian Arthur's homepage at the Santa Fe Institute Transcript Brian Arthur [00:00:00]  In this conversation, Dr. Brian Arthur. And I talk about how technology can be modeled as modular and evolving system. Commentorial evolution more broadly, and we dig into some fascinating technological hae studies that informed your book, his book, the nature of tech. Brian is a researcher and author who is perhaps best known for his work on complexity economics. Uh, but I wanted to talk to him [00:01:00] because of the fascinating work he's done, building out theories of technology. Uh, as we discussed in the podcast, there's been a lot of theorizing around science, you know, with the works of popper and Kuhn and other. But there's has been much less rigorous work on how technology works despite its effect on our lives. As some background, Brian currently works at park formerly Xerox park, the birthplace of the personal computer, and has also worked at the Santa Fe Institute and was a professor at Stanford university before that. Uh, so without further ado, here's my conversation with Brian Arthur.  Mo far less interested in technology. So if anybody asks me about technology immediately search. Sure. But so the background to this is that mostly I'm known for a new framework and economic theory, which is called complexity economics. I'm not the [00:02:00] only developer of that, but certainly one of the fathers, well, grandfather, one of the fathers, definitely. I was thinking one of the co-conspirators I think every new scientific theory like starts off as a little bit of a conspiracy. Yes, yes, absolutely. Yeah. This is no exception anyways. So that's what I've been doing. I'm I've think I've produced enough papers and books on that. And I would, so I've been in South Africa lately for many months since last year got back about a month ago and I'm now I was, as these things work in life, I think there's arcs, you know, you're getting interested in something, you work it out or whatever it would be. Businesses, you [00:03:00] start children, there's a kind of arc and, and thing. And you work all that out. And very often that reaches some completion. So most of the things I've been doing, we've reached a completion. I thought maybe it's because I getting ancient, but I don't think so. I think it was that I just kept working at these things. And for some reason, technologies coming back up to think about it in 2009, when this book came out, I stopped thinking about technology people, norm they think, oh yeah, you wrote this book. You must be incredibly interested. Yeah. But it doesn't mean I want to spend the rest of your life. Just thinking about the site, start writing this story, like writing Harry Potter, you know, it doesn't mean to do that forever. Wait, like writing the book is like the whole [00:04:00] point of writing the book. So you can stop thinking about it. Right? Like you get it out of your head into the book. Yeah, you're done. So, okay. So this is very much Silicon valley and I left academia in 1996. I left Stanford I think was I'm not really an academic I'm, I'm a researcher sad that those two things have diverged a little bit. So Stanford treated me extraordinarily well. I've no objections, but anyway, I think I'd been to the Santa Fe Institute and it was hard to come back to standard academia after that.  So why, should people care about sort of, not just the output of the technology creation process, but theory behind technology. Why, why does that matter? Well[00:05:00]  I think that what a fine in in general, whether it's in Europe or China or America, People use tremendous amount of technology. If you ask the average person, what technology is, they tell you it's their smartphone, or it's catch a tree in their cars or something, but they're, most people are contend to make heavy use of technology of, I count everything from frying pans or cars but we make directly or indirectly, enormously heavy use of technology. And we don't think about where it comes from. And so there's a few kind of tendencies and biases, you know we watch we have incredibly good retinal displays these days on our computers. [00:06:00] We can do marvelous things with our smartphone. We switch on GPS and our cars, and very shortly that we won't have to drive at all presumably in a few years. And so all of this technology is doing marvelous things, but for some strange reason, We take it for granted in the sense, we're not that curious as to how it works. People trend in engineering is I am, or I can actually tell you that throughout my entire life, I've been interested in how things work, how technology works, even if it's just something like radios. I remember when I was 10, I like many other kids. I, I constructed a radio and a few instructions. I was very curious how all that worked and but people in general are not curious. So I [00:07:00] invite them quite often to do the following thought experiments. Sometimes them giving talks. All right. Technology. Well, it's an important, yeah, sort of does it matter? Probably while I would matter. And a lot of people manage to be mildly hostile to technology, but there are some of the heaviest users they're blogging on there on Facebook and railing about technology and then getting into their tech late and cars and things like that. So the thought experiment I like to pose to people is imagine you wake up one morning. And for some really weird or malign reason, all your technology is to super weird. So you wake up in your PJ's and you stagger off to the bathroom, but the toilet, [00:08:00] you trying to wash your hands or brush your teeth. That is no sink in the bathroom. There's no running water. You scratch your head and just sort of shrugged in you go off to make coffee, but there's no coffee maker, et cetera. You, in this aspiration, you leave your house and go to clinch your car to go to work. But there's no car. In fact, there's no gas stations. In fact, there's no cars on the roads. In fact, there's no roads and there's no buildings downtown and you're just standing there and naked fields. And wondering, where does this all go? And really what's happened in this weird Saifai set up is that let's say all technologies that were cooked up after say 1300. So what would that be? The last 700 years or so? I've disappeared. And and you've [00:09:00] just left there and. People then said to me, well, I mean, wouldn't there have been technologies then. Sure. So you know how to, if you're a really good architect, you might know how to build cathedrals. You might know how to do some stone bridges. You might know how to produce linen so that you're not walking around with any proper warm clothes and so on. But our whole, my point is that if you took away everything invented. So in the last few hundred years, our modern world or disappear, and you could say, well, we have science, Peter, but without technology, you wouldn't have any instruments to measure anything. There'd be no telescopes. Well, we still have our conceptual ideas. Well, we would still vote Republican or not as the case may be. Yeah, you'd have, and I'd still have my family. Yeah. But how long are your kids gonna [00:10:00] live? Because no modern medicine. Yeah, et cetera. So my point is that not only does technology influence us, it creates our entire world. And yet we take this thing that creates our entire world. Totally. For granted, I'd say by and large, there are plenty of people who are fascinated like you or me, but we tend to take it for granted. And so there isn't much curiosity about technology. And when I started to look into this seriously, I find that there's no ology of technology. There's theories about where science comes from and there's theories about music musicology and theories, endless theories about architecture and, and even theology. But there isn't a very [00:11:00] well-developed set of ideas or theories on what technology is when, where it comes from. Now, if you know, this area is a, was that true? On Thur, you know, I could mention 20 books on it and Stanford library, but when I went to look for them, I couldn't find very much compared with other fields, archi, ology, or petrol energy, you name it technology or knowledge. It was, I went to talk to a wonderful engineer in Stanford. I'm sure he's no longer alive. Cause this was about 15 years ago. He was 95 or so if I couldn't remember his name it's an Italian name, just a second. I brought this to prompts. Just a sec. I'm being sent to you. I remember his name and [00:12:00] make it the first name for him. Yeah. Walter VIN sent him. So I went to see one it's rarely top-notch aerospace engineers of the 20th century had lunch with them. And I said, have engineers themselves worked out a theory of the foundations of their subject. And he looked, he sort of looked slightly embarrassed. He says, no. I said, why not? And he paused. He was very honest. He just paused. And he says, engineers like problems they can solve. It's. So compared with other fiel

In this Conversation, Jason Crawford and I talk about starting a nonprofit organization, changing conceptions of progress, why 26 years after WWII may have been what happened in 1971, and more. Jason is the proprietor of Roots of Progress a blog and educational hub that has recently become a full-fledged nonprofit devoted to the philosophy of progress. Jason's a returning guest to the podcast — we first spoke in 2019 relatively soon after he went full time on the project . I thought it would be interesting to do an update now that roots of progress is entering a new stage of its evolution.   Links Roots of Progress Nonprofit announcement Transcript So what was the impetus to switch from sort of being an independent researcher to like actually starting a nonprofit I'm really interested in. Yeah. The basic thing was understanding or getting a sense of the level of support that was actually out there for what I was doing. In brief people wanted to give me money and and one, the best way to receive and manage funds is to have a national nonprofit organization. And I realized there was actually enough support to support more than just myself, which had been doing, you know, as an independent researcher for a year or two. But there was actually enough to have some help around me to basically just make me more effective and, and further the mission. So I've already been able to hire research [00:02:00] assistants. Very soon I'm going to be putting out a a wanted ad for a chief of staff or you know, sort of an everything assistant to help with all sorts of operations and project management and things. And so having these folks around me is going to just help me do a lot more and it's going to let me sort of delegate everything that I can possibly delegate and focus on the things that only I can do, which is mostly research and writing. Nice and sort of, it seems like it would be possible to take money and hire people and do all that without forming a nonprofit. So what what's sort of like in your mind that the thing that makes it worth it. Well, for one thing, it's a lot easier to receive money when you have a, an organization that is designated as a 5 0 1 C three tax status in the United States, that is a status that makes deductions that makes donations tax deductible. Whereas other donations to other types of nonprofits are not I had had issues in the past. One organization would want to [00:03:00] give me a grant as an independent researcher, but they didn't want to give it to an individual. They wanted it to go through a 5 0 1 C3. So then I had to get a new. Organization to sort of like receive the donation for me and then turn around and re grant it to me. And that was just, you know, complicated overhead. Some organizations didn't want to do that all the time. So it was, it was just much simpler to keep doing this if I had my own organization. And do you have sort of a broad vision for the organization? Absolutely. Yes. And it, I mean, it is essentially the same as the vision for my work, which I recently articulated in an essay on richer progress.org. We need a new philosophy of progress for the 21st century and establishing such a philosophy is, is my personal mission. And is the mission. Of the organization to just very briefly frame this in the I, the 19th century had a very sort of strong and positive, you know, pro progress vision of, of what progress was and what it could do for humanity and in the [00:04:00] 20th century. That optimism faded into skepticism and fear and distrust. And I think there are ways in which the 19th century philosophy of progress was perhaps naively optimistic. I don't think we should go back to that at all, but I think we need a, we need to rescue the idea of progress itself. Which the 20th century sort of fell out of love with, and we need to find ways to acknowledge and address the very real problems and risks of progress while not losing our fundamental optimism and confidence and will to, to move forward. We need to, we need to regain to recapture that idea of progress and that fundamental belief in our own agency so that we can go forward in the 21st century with progress. You know, while doing so in a way that is fundamentally safe and benefits all of humanity. And since you, since you mentioned philosophy, I'm really like, just, just ask you a very weird question. That's related to something that I've been thinking about. And [00:05:00] so like, in addition to the fact that I completely agree the philosophy. Progress needs to be updated, recreated. It feels like the same thing needs to be done with like the idea of classical liberalism that like it was created. Like, I think like, sort of both of these, these philosophies a are related and B were created in a world that is just has different assumptions than we have today. Have you like, thought about how the, those two, like those two sort of like philosophical updates. Yeah. So first off, just on that question of, of reinventing classical liberalism, I think you're right. Let me take this as an opportunity to plug a couple of publications that I think are exploring this concept. Yeah. So so the first I'll mention is palladium. I mentioned this because of the founding essay of palladium, which was written by Jonah Bennet as I think a good statement of the problem of, of why classical liberalism is [00:06:00] or, or I think he called it the liberal order, which has maybe a slightly different thing. But you know, the, the, the basic idea of You know, representative democracy is you know, or constitutional republics with, with sort of representative democracy you know, and, and basic ideas of of freedom of speech and other sort of human rights and individual rights. You know, all of that as being sort of basic world order you know, Jonah was saying that that is in question now and. There's essentially now. Okay. I'm going to, I'm going to frame this my own way. I don't know if this is exactly how gender would put it, but there's basically, there's, there's basically now a. A fight between the abolitionists and the reformists, right. Those who think that the, the, the, that liberal order is sort of like fundamentally corrupt. It needs to be burned to the ground and replaced versus those who think it's fundamentally sound, but may have problems and therefore needs reform. And so you know, I think Jonah is on the reform side and I'm on the reform side. I think, you know, the institutions of you know, Western institutions and the institutions of the enlightenment let's say are like [00:07:00] fundamentally sound and need reform. Yeah, rather than, rather than just being raised to the ground. This was also a theme towards the end of enlightenment now by Steven Pinker that you know, a lot of, a lot of why he wrote that book was to sort of counter the fundamental narrative decline ism. If you believe that the world is going to hell, then it makes sense to question the fundamental institutions that have brought us here. And it kind of makes sense to have a burn it all to the ground. Mentality. Right. And so those things go together. Whereas if you believe that you know, actually we've made a lot of progress over the last couple of hundred years. Then you say, Hey, these institutions are actually serving us very well. And again, if there are problems with them, let's sort of address those problems in a reformist type of approach, not an abolitionist type approach. So Jonah Bennett was one of the co-founders of palladium and that's an interesting magazine or I recommend checking out. Another publication that's addressing some of these concepts is I would say persuasion by Yasha Munk. So Yasha is was a part of the Atlantic as I recall. [00:08:00] And basically wanted to. Make a home for people who were maybe left leaning or you know, would call themselves liberals, but did not like the new sort of woke ideology that is arising on the left and wanted to carve out a space for for free speech and for I don't know, just a different a non-local liberalism, let's say. And so persuasion is a sub stack in a community. That's an interesting one. And then the third one that I'll mention is called symposium. And that is done by a friend of mine. Roger Sinskey who it himself has maybe a little bit more would consider himself kind of a more right-leaning or maybe. Just call himself more of an individualist or an independent or a, you know, something else. But I think he maybe appeals more to people who are a little more right-leaning, but he also wanted you know, something that I think a lot of people are, are both maybe both on the right and the left are wanting to break away both from woke ism and from Trumpism and find something that's neither of those things. And so we're seeing this interesting. Where people on the right and left are actually maybe [00:09:00] coming together to try to find a third alternative to where those two sides are going. So symposium is another publication where you know, people are sort of coming together to discuss, what is this idea of liberalism? What does it mean? I think Tristan ski said that he wanted some posting to be the kind of place where Steven Pinker and George will, could come together to discuss what liberalism means. And then, then he like literally had that as a, as a podcast episode. Like those two people. So anyway, recommend, recommend checking it out. And, and Rob is a very good writer. So palladium, persuasion and symposium. Those are the three that I recommend checking out to to explore this kind of idea of. Nice. Yeah. And I think it looks, I mean, I mean, I guess in my head it actually like hooks, like it's sort of like extremely coupled to, to progress. Cause I think a lot of the places where we, there's almost like this tension between ideas of classical liberalism, like property rights and things that we would like see as progress. Right. Cause it's like, okay, you want to build your [00:

In this conversation, Dr. Stephen Dean talks about how he created the 1976 US fusion program plan, how it played out and the history of fusion power in the US, technology program planning and management more broadly, and more. Stephen has been working on making fusion energy a reality for more than five decades. He did research on controlled fusion reactions in the 60s and in the 70s became a director at the Atomic energy commission which then became the Energy Research and Development Administration which *then* became the department of energy. In 1979 he left government to form the consultancy Fusion Power associates, where he still works. In 1976, he led the preparation of a report called "Fusion power by magnetic confinement" that laid out a roadmap of the work that would need to be done to turn fusion from a science experiment into a functional energy source. References Fusion Power by Magnetic Confinement Executive Summary Volume 1 Volume 2 Volume 3 Volume 4 Fusion Power Associates The notorious fusion never plot Adam Marblestone on technological roadmapping My hypotheses on program design (which were challenged by this conversation!) Fusion Energy Base (a good website on fusion broadly) ITER Transcript  (Machine generated, so please excuse errors) [00:00:00]  In this conversation, Dr. Steven Dean, and I talk about how he created the 1976 S fusion program plan, how it played out in the history of fusion power in the U S technology program, planning and management more broadly, and even more things. Steven has been working on making fusion energy a reality for more than five decades. He did research on control, fusion reactions in the 1960s and seventies, he became a director [00:01:00] at the atomic energy commission, which then became the energy research and development of administration, which then became the department of energy in 1979. He left government to form the consultancy fusion, power associates, where you still want. In 1976, he led the preparation of a report called fusion power by magnetic confinement that laid out a roadmap of the work that needed would need to be done to turn fusion from a science experiment, into a functional energy source. And if I can sort of riff about this for a minute, the thing is. Unlike what I sort of see as modern roadmaps, it lays out not just the sort of like plan of record to getting fusion, to be a real energy source, but lays out all the different possible scenarios in terms of funding, in terms of new technology that we can't even think of being created and lays everything. Yeah. In a way that you can actually sort of make decisions off of it. [00:02:00] And I think one of the most impressive things is that it has several different what it calls logics of funding, which is like different, different funding levels and different funding curves. And it actually, unfortunately, accurately predicts that if you fund fusion below a certain level, even if you're funding it continually you'll never get to. An actual useful fusion source because you'll never have enough money to build these, these demonstrator missions. And so in a way it's sort of predicts the future. This, this document is super impressive. If you haven't seen it you should absolutely check it out there. There are links in the show notes and it's sort of, one of the reasons I wanted to talk to Dr. Dean is because this, this document. Is one of the pieces of evidence behind my hypothesis. That to some extent, program design and program management for advanced technologies is a bit of a lost art. And so I wanted to learn more about how he thought about it and built [00:03:00] it. So without further ado, here's my conversation with Steven Dean. To start off, what was the context of creating the fusion plan? Well, I guess I would have to say that it started a few years earlier in a sense that in 1972 the I was in the fusion office and in the atomic energy commission and the office of men and mission management and budget at the white house put out instructions to, I guess, all the agencies that they should prepare an analysis of their programs under a system, they called management by objectives. And this was some, this was a formalism that was, had a certain amount of popularity at that time. And I was asked to prepare something on the fusion program as a part of the agency, doing this for all of its programs. And [00:04:00] in doing that I looked at our program and I Laid out a map basically that showed the different parts of the program on a map like a roadmap and what the timelines might be and what the functions of those of facilities would be. And when the decisions might be and what decisions would work into into, into what, and that was never published in, in a report, but it w except internally, but the map itself was published and widely distributed. And I have it on my wall and it's in my book. So that was the first, my first venture into. Into doing something that resembled plan, it was not a detailed plan, but it was an outline of decision points and flow this sort of a flow diagram, but it did connect all the different parts of the [00:05:00] program and the identified sub elements, you know, not in great detail and, and budgets were not asked for at that time. So that's how I got into this idea and a little experience in, in the planning area. And then a few years later, we had the gasoline crisis in the U S where there were long lines and we couldn't get gas and people were sitting in their cars over overnight. And the, the white house at that time said that you know, we had to become energy, independent oil you know, the OPEC. And, and so Bob Hirsch, who was at that time about to transition from the director of the fusion program to an assistant minister traitor of Urdu in, I think it was 74, late 74, 75. The, the government decided to Congress decided, or the [00:06:00] administration decided to abolish the atomic energy commission and transition it into something called the energy research and development administration or arena. And the reason for that was to. It create an agency whose function was clearly for all of energy and not just for atomic energy in order to respond to the energy crisis and to get us off of the dependence on foreign oil imports for, for vehicles and things. And so when, when, when that happened, my boss, who was Bob Hirsch at the time he became, he was actually appointed in assistant administrator of errata for basically all the long range energy programs, which included fusion. And as he was at transition, he, he came up with the idea that we should create a detailed long range plan for the, [00:07:00] for the program. And he, he was obviously becoming sort of a senior manager for the many things and he wasn't certainly going to try and do this himself. And so he and I were very close. I was at that point he had three divisions in the fusion program and I was the director of the largest division, which had all of the main experimental programs. And so he asked me to prepare this plan. And if you look at the plan at the very beginning, there's this there's a chart that shows Bob's basically guidance, which was to note that that there needed to be a multiplicity of pathways because no one organization or, or group or division or program was in response could be in full control. And that in order to have a plan that might have some hope of [00:08:00] Last thing that you had to take into account a number of policy variables he said, and technical variables, which meant that he said, because need for the, for the, for fusion and the intent of the government and the funding is all in control by other people in the government. We had to have a number of plans by which the program could be conducted. So he came up with the idea that, well, let's have five plans, which he called logic. So he basically created that framework and turned it over to me at the beginning, I guess, of 1975, I think it was. And to, to create this. This plant. So that's how it all got started. And I had been doing a number of things with the program in terms of the major [00:09:00] experiments that were under my control as a director of the confinement systems, division magnetic confinement systems. I was forcing all, all the people that were that whose budget I had to control over to, to tell me what they were doing and what they needed to do. And so on. It's all right though, I had already been and working on a lot of these things in, within my area, but at that point I took over the responsibility of creating the, in the entire plant. And so I, I, I took it over and I started I created a, a small working group within our office. And we added people that we thought were responsible that could do this for us, or give us the details out in the various parts of the program, all elements of the program. And we created a team and we, we launched this and and this was the result. We were determined to look to these five [00:10:00] logics. They ranged from both, you know, basically a steady level of effort to a maximum level of effort. And and we just started creating these things. During that six months, first six months of 1976, And this was the result. Nice. And did you, so, so each of the logics is kind of a, a wiggly curve. Did, did you go in knowing what the shape of the funding curve for each logic would be, or did you just go in with the framework that there would be five logics and over the course of designing the program, you figured out what the actual shape of those curves would be? Well, we created a definition, a rough definition of what each of the logics was supposed to look like, not in detail, but for example, a [00:11:00] logic to what says moderately. Expanding. But the tech progress would be limited by the availability of funds. But new projects were not started unless we knew that funds would be available. And so we knew that we could not address a lot of problems i

Eli Dourado on how the sausage of technology policy is made, the relationship between total factor productivity and technological progress, airships, and more. Eli is an economist, regulatory hacker, and a senior research fellow at the Center for Growth and Opportunity at Utah State University. In the past, he was the head of global policy at Boom Supersonic where he navigated the thicket of regulations on supersonic flight. Before that, he directed the technology policy program at the Mercatus Center at George Mason University.. Eli's Website Eli on Twitter Transcript audio_only [00:00:00] In this conversation, Eli Durado. And I talk about how the sausage of technology policy has made the relationship between total factor productivity and technological progress, airships, and more Eli is an economist regulatory, hacker, and senior research fellow at the center for growth and opportunity at Utah state university. In the past, he was the head of global policy at boom supersonic, [00:01:00] where he navigated the thicket of regulations on superstar. Before that he directed the technology policy program at the Mercatus center at George Mason university. I wanted to talk to Eli because it feels like there's a gap between the people who understand how technology works and the people who understand how the government works. And Isla is one of those rare folks who understands both. So without further ado my conversation with Eli Dorado.  So just jump directly into it.  When you were on a policy team, what do you actually do?  Well that depends on which policy team you're on. Right. So, so in my career you mean, do you mean the, in sort of like the, the public policy or like the research center think tanks kind of space or in, in, in a company because I've done both. Yeah, exactly. Oh, I didn't even realize that you do like that. It's like different things. So so like, I guess, like, let's start with [00:02:00] Boom. You're you're on a policy team at a technology company and. Yeah. Yeah. So when I, when I started at boom so we had a problem. Right. Which was like, we needed to know what landing and takeoff noise standard we could design too. Right. Like, so, so we needed to know like how loud the airplane could be.  And how, how quiet it had to be. Right. And, and as a big trade off on, on aircraft performance depending on that. And so when I joined up with boom, like FAA had a, what's called a policy statement. Right. Which is, you know, some degree of binding, but not really right. Like that they had published back in 2008 that said, you know, we don't have standards for supersonic airplanes, but you know, like when we do create them they, you know, they're during the subsonic portion of flight, we anticipate the subsidy Arctic standards. Right. So, so for, [00:03:00] for, for landing and takeoff, which is like the big thing that we are concerned about, like that's all subsonic. So we, you know, so that sort of the FAA is like going in position was like, well, the subsonic standards apply to, to boom. And so I kind of like joined up in early 2017 and sort of my job was like, let's figure out a way for that, not to be the case. Right. And so it was, it was basically, you know, look at all the different look at the space of actors and try to figure out a way for that, not to be true. And so, and so that's like kind of what I did. I started, you know, started talking with Congress with FAA. I started figuring out what levers we could push, what, what what angles we could Work work with to ensure that that, that we have we've got to a different place, different answer in the end. And, and so the, like, so basically it's just like this completely bespoke process of [00:04:00] totally like, even trying to figure out like what the constraints you're under are. Exactly. Right. So, so yeah, so it was, there's like a bunch of different, different aspects of that question, right? So there will you know, there's, there is statute, you know, congressional laws passed by Congress that had a bearing on the answer to that question that I went back to like the 1970s. And before there w you know, there was the FAA policy statement. There was, of course the FAA team, which you had to develop, you know you know, relationships with and, and, and, and sort of work with you have the industry association, right. That we remember of that Had different companies, you know, in addition, you know, in addition to boom, there, there were a bunch of other companies Ariane, which is no longer operating. We had Gulf stream, which no longer has a supersonic program. Or actually they didn't Edward admitted to having it announced really dead. They, you know, there was, you know, GE and rolls Royce. And so you had all these companies like coming together, you know, sort of under the, [00:05:00] under the watchful eye of Boeing, of course also. And, and so like the industry association had to have a position on things, and then you had like the international aspect of it. So you had a, there's a UN agency called Oko that sort of coordinates aviation standards among all the different countries you had the European regulators who did not like this idea that there were American startups doing Supersonics because, because the European companies weren't going to do it. And so they wanted to squash everything and they were like, no, no subsonic standards totally applied. Right. And so so that was, that's really the. The environment that, you know, sort of, I came into and I was like, okay, I've got to figure out, you know, I've got to figure out, build a team and, and, and figure out an approach here. And and, and try to try to make it not be the case that the subsonic centers apply. So we, so, you know, basically we tried a bunch of things at first, right. Like we tried to like, get our industry association, like all geared up for like, okay, well, we've gotta, we gotta fight this and they didn't want to do that. Right. So like, like [00:06:00] the other people didn't want to do that. Right. We tried a bunch of different angles in terms of, you know, we, we, what we ended up doing w w we got Congress to get excited about it and sort of, they, they started to, you know, there was a.  Sort of a draft bill that had some, some very forward-leaning supersonic language that we, we you know, worked with Congress on it never passed in exactly that form, but it passed later in the 2018 FAA reauthorization. And then the thing that actually kind of ended up working was I had this idea in late 2017 was, well, you know, what. The, the sub the subsonic standard changes at the end of this year. Right. So, so so the end of 2017, so I was like, well, let's apply for type certification this year. Right. So we applied, like, we are nowhere close to an airplane. Right. And know we're close. Right. Right. And I was like, well, let's just, let's just, let's just like, screw it. We're going to apply like, like in 2017. And I had to like, get the execs to sign off on that. Right. We're going to do it, but we did. [00:07:00] So by the end of, I think December, 2017, we applied, I of course, you know, talk to my FFA colleagues and told them like, Hey, we're going to apply. Just so you know, they're like, well, that raises a whole bunch of questions. And, and that sort of got it, got them working down this path where they were like, well, you only have under part 36 of the FAA rules. You only have five years to to keep that noise standard. If, if you apply today and you're probably not gonna be done in five years. And I was like, that's true. We're probably not going to be done in five years, but we think that part 36 doesn't apply to us at all right. The way it's written. And then they went back and then they looked at it and they were like, oh, Part 36 doesn't apply to them like they're right. Like, you know, Eli's the first person in the history of Supersonics three per 36 and very closely. Right. And so and so then they went back and they like talked to their lawyers and, you know, they, I think came up with a new position in a new legal interpretation [00:08:00] w basically a memo that, that was, that was published that was like, okay, the subsonic standards don't apply and we don't have standards. We can start making some standards. And if we don't have one at any time for any particular applicant, we can make one for that applicant. We can, it's called the rule of particular applicability. So that kind of, once we got that, then in early 2018, like that kind of solved their problem. Like, and I think in in at least th th the domestic part didn't solve the international part, like from, from from Europe and so on. So. I mean, I, so, so if you think about like, what do you do on a policy team? Like you figure out like how, you know, how, how do you solve the problem that you have, that, that you were, that you were hired hard to fix and you just try things, try things until something works. It's part of the answer. Yeah. That's I mean, that's, I really appreciate you going into that level of detail because it's like the sort of like affordances of these things seem incredibly opaque. And just [00:09:00] for, for context, the subsonic standards are the standards that do not a lot, like that set a very like low noise bar. It's very stringent. I mean, the modern, the modern standards are pretty stringent. Like it used to be like, you couldn't, you couldn't basically like stand on a runway and have a conversation while plane's taken off these days. Like, I mean, it's, it's, it's gotten very, very impressive, but they, you know, the, the modern planes have gotten that way because they have high bypass ratios and the engines like big, big fans that move a lot of air around the engine core, not through it. Right. And so so that is, you know, that's just not workable when you're kind of trying to push that big fan through, you know, through the air at mock you know, 2.2 is what we were

In this conversation I talk to the Amazing Arati Prabhakar about using Solutions R&D to tackle big societal problems, gaps in the innovation ecosystem, DARPA, and more. Arati's career has covered almost every corner of the innovation ecosystem - she's done basically every role at - DARPA she was a program manager, started their Microelectronics Technology Office, and several years later returned to server as its Director. She was also the director of the National Institute of Standards and Technology and was a venture capitalist at US venture partners. Now she's launching Actuate - a non-profit leveraging the ARPA model to go after some of the biggest problems in American society. Links Actuate Website In the Realm of the Barely Feasible - Arati's Article about Actuate and Solutions R&D Arati on Wikipedia  Transcript [00:00:00] welcome to idea machines. I'm your host and Reinhart. And this podcast is a deep dive into the systems and people that bring innovations from glimmers in someone's eye, all the way to tools, processes, and ideas that can shift paradigms. We see these systems outputs everywhere, but what's inside the black boxes with guests. I dig below the surface into crucial, but often unspoken questions. To explore themes of how we enable innovations today and how we could do it better tomorrow. In this conversation, I talked to the amazing RFE provoca about using solutions R and D tackle, big societal problems, gaps in the innovation ecosystem, DARPA and more. Are these career has covered almost every corner of the innovation ecosystem. She's done almost every job at DARPA where she was a program manager, started their micro electronics technology office. And several years later returned serve as their [00:01:00] director. She was also the director at the national Institute of standards and technology and a venture capitalist at us venture partners. Now she's launching actuate a nonprofit leveraging the ARPA model to go after some of the biggest problems in American society. Hope you enjoy my conversation with Arthur. Provoca.  I'd love to start off and sort of frame this for everybody is with a quote from your article, which, which everybody should read and which I will link to in the show notes. You say yet, we lack a systemic understanding of how to nurture the sort of rich ecosystem we need to confront the societal changes facing us. Now over 75 years, the federal government has dramatically increased supportive research and universities and national labs have built layers of incentives and deep culture for the research role. Companies have honed their ability to develop products in markets, shifting away from doing their own fundamental research in established industries, American venture capital and entrepreneurship have supercharged the startup pathway for commercialization in some [00:02:00] sectors, but we haven't yet put enough energy into understanding the bigger space where policy finance and the market meet to scale component ideas into the kind of deep and wide innovations that can solve big previously intractable problems in society. These sorts of problems, aren't aligned to tangible market opportunities or to the missions of established government R and D organizations today, the philanthropic sector can play a pivotal role by taking the early risk of trying new methods for R and D and developing initial examples that governments and markets can adopt and ramp up the hypothesis behind actuate is that solutions R and D can be a starting place for catalyzing the necessary change in the nation's innovation ecosystem. And so with that, with those, I think I want to test it in a nutshell exactly like that. So can we start with how do you see solutions R and D as being different from other R D and, and sort of coupled with that? How is actuate different from other non-profits. Yeah, I think [00:03:00] that's, that's one of the important threads in this tapestry that we want to develop. So solutions R and D let's see. I think those of us who live in the world of R and D and innovation are very familiar with basic research. That that is about new knowledge, new exploration, but it's designed all the incentives, all the funding and the structures are designed to have that end with publishing papers. And then on the other hand, there's. But the whole machinery that turns an advance into, you know, takes a technological advance or a research advance and turns it into the changes that we want in society that could be new products and services. It could be new policies, it could be new practices and that implementation machinery. The market companies, policymaking, what individuals choose to do pilot practices. I think we understand that. And there are places where the, you know, things just move from basic research over into actual [00:04:00] implementation. But in fact, there are, there are a lot of places where that doesn't happen, seamlessly and solutions, R and D is this weird thing in the middle. That builds on top of a rich foundation of basic research. It has it, its objective is to demonstrate and to prove out completely radically better ways. To solve problems or to pursue different opportunities so that they can be implemented at scale. And so it has this hybrid character that it is at the one on one hand, it's very directed to specific goals. And in that sense, it looks more like. Product development and marching forward and, you know, boom, boom, boom, make things happen, execute drive to drive, drive to an integrated goal. And on the other hand it requires a lot of creativity, experimentation risk-taking. And so it has some of those elements from the research side. So it's this middle [00:05:00] kingdom that I. Love because it has, I think it just has enormous leverage. And I, you know, I, I think a couple of points, number one, it's it requires to do it well, requires its own. Types of expertise and practices and culture that are different from either the research or implementation. And secondly, I would say that it, I think it's overall in the U S in the current us innovation system. I think it's something of a gap. There, there, there, there, there are many, many areas where we're not doing it as well as we need to. And then for some of the new problems, which I hope we'll talk about as well. I think it's actually a very interesting lever to boot the whole system up that we're going to need going forward. Yeah. And so actually just piggybacking right off of that, you've outlined three major sort of problems that you're tackling initially. Climate change sort of health, like general American [00:06:00] health and data privacy. I'm actually really interested in, like, what was the process of deciding, like, these are the things that we're going to work on. Yeah, but this whole actuate emerged from a thought process from a lot of. Bebe's rattling around in the box car in my head in the period as I was wrapping up at DARPA in 2016, at the end of 2016 and going into 2017 when I left and what I was thinking about was how phenomenally good our innovation machinery is. For the problems that we set out to tackle at the end of the second world war, that agenda was national security technology for economic growth. A lot of that was information technology. We set out to tackle health. Instead we did biomedicine. We went long on biomedicine, didn't break their left, left a lot of our serious health problems sitting on the shelf and a big agenda was funding, basic research and, and we've executed on that agenda. That's what we are [00:07:00] very, very, very good at what I couldn't stop thinking about. As I was wrapping up at DARPA is the problems that I think will, you know, many of us feel will determine whether we succeed or fail as a society going forward. So it's not that these challenges, you know, national security or how it's not that those problems have gone away and we should stop. It's just that we have some things that will break us at our. Yeah, arguably, they are in the process of breaking us. If we don't deal with them right now, one is access to opportunity for every person in our society. A second is population health at a cost that doesn't break the economy. Another is being able to trust data and information and the information age in which we now live. And the forest obviously is mitigating climate change. And if you think about it, these, these were not, but these weren't the top of mind issues at the end of the second world war, right? I mean, we had other problems. We didn't really know what to do about. So some of these are all problems that we didn't really know what to do about. Some of these are new problems. And, [00:08:00] and so, you know, now here we are in 2021, if you say what's what really matters those were the four areas that we identified that. Are critical to the success of our society. Number one, number two, we aren't succeeding. And that means we need innovation of all different types. And number three, we, we don't, we're not innovating, you know, we're either innovating at the zero billion dollars a year level, or we are spending money on R and D, but it's not yet turning the tide of the problem and, and that, so that's how we ended up focusing on those areas. Got it. And what could you actually, like, I, I love digging into sort of the nitty gritties of like, what was the process of designing these, these programs? Right. So just to sort of scope this a little bit, these broad areas that I'm talking about, I think of as. But the major societal challenges that we face today, actuate, which is a tiny early stage seed stage [00:09:00] nonprofit organization. Our our aspiration is over time to build portfolios of solutions, R and D programs. In each of these areas. And so very, you know, you, you, you made reference to a couple of the specific programs. One is about being able to access many more data sets to mine, their insights by cross-linking acro

In this conversation I talk to Ilan Gur about what it really means for technology to "escape the lab", the power of context to shape the usefulness of research, the inadequacies of current institutional structures, how activate helps technology escape the lab *by* changing people's context, and more. Ilan is the CEO and founder of Activate, which is a nonprofit that runs a fellowship enabling scientists to spend two years embedded in research institutions to mature technology from a concept to a first product. In the past, he has also served as a program director at ARPA-E and was a cofounder of Seeo, where he commercial new high-density battery technology. Links Activate Ilan on Twitter Ilan on My Climate Journey Podcast  Transcript In the past, we've talked about the, how the whole process of really turning hardcore scientific research into products that have an impact on people's lives is fairly abstract to people outside of the system. Since you've both walked the path and now help other people do the same, let's round the conversation. would you go into detail on what the actual actions you need to take to go from say, being a graduate student who just published a paper on a promising battery technology to an improved battery in a car. That's that's a great place to start. let me try and answer that from a few different dimensions. I'll, I'll start by answering it, just from an anecdote about my personal experience, which I've shared in other places, but, you know, I basically. Went into my PhD program because I felt like the field I was studying material scientists, material science could, be the biggest way to make a big impact on climate change by basically taking new science and turning it into the next generation of all the technologies. We need to have a sustainable economy. And, I was working in nanotechnology, joined. Kind of the world, the best research group in the world that that was working on how nano materials could improve solar cells. and this is before the, the enormous solar market that exists today exists. There was a sense at the time that, you know, we needed a completely new generation of technology to make solar ubiquitous and cost effective. And so, you know, we had this great mantra around how we were going to print solar cells like newspapers, using these small colloidal nano, semiconductors. and the research was phenomenal. we were driven by the fact that what I like to say is, you know, we wrote a science paper where the first paragraph, like any, talked about how the research was going to change the world. And it wasn't until I randomly got connected with some business school folks at Berkeley, where I was doing my PhD. and they actually. It didn't take long. they put me through just a few cycles of digging one level deeper into, how solar cells were actually made, how they were sold, what determined their, their costs and the cost of energy they produce. and I ended up, you know, over the course of a few weeks with a spreadsheet that I still have somewhere, which told me that. If we hit all of our targets and our research in terms of what we thought could change the world. we would end up with a solar cell where even if you gave it away for free, it couldn't compete with the existing state of the art Silicon solar cells at the time. and it was a really. Simple idea, which was, we were making dirt cheap solar cells, but they probably wouldn't last very long. And we didn't think that was such a big deal. You just print some more. and yet, certainly at the time, and it's still true. It's such a, such a predominant amount of the cost of solar energy came from the balance of systems and installations. And I bring up the story because, for me, it was a tipping point. We had so much excitement about our research. It was even published in Forbes, you know, so a business magazine, and. It just showed how it showed, how easy it was to think you were doing something productive and successful. I it's not that I, I, I was in academia, but the reason I was there was to try and get something productive that could turn into a product. Right. And I had missed the boat so much, even with that intention. and so that was a shock to me. And so. That was kind of the first lesson around how, you know, institutions matter and incentives matter. but what I ended up doing was then leaving academia and jumping into an early stage startup, which was an amazing vehicle to think about how this transition happens and, you know, basically the learning there, and, This is what we now, you know, this is a lot of what we now indoctrinate and try and help people understand in the fellowship we run, was that, you know, the depth and multitude of elements that determine whether a technology can actually make it from the research stage to a product in the market. You know, first of all, you know, the idea is like, you know, the easy part in some regard. but yeah. You know, the number of levels deeper, you have to go to understand, okay, how is it going to be, how is it actually going to be valuable? Who's going to buy it. Why are they going to buy it? You know, how does, how does the whole system get built to make it, it's it's a month multi-dimensional problem where everything needs to line up between finance and the team you have in the market yet. And it's technology. and. You know, for me, I think, you know, this we've talked before, one of the biggest things that I've come to realize is we've got, you know, we've got hundreds of billions of dollars that government spends to do the idea and ideation. We've got hundreds of billions of dollars that the private sector spends to basically take the early prototypes and the idea of a product and scale it. and we've got really very little, that goes into how you do all the really hard stuff of translating one to the other. Yeah. So, so let's like what I'm going to actually continue to poke at. Like, what is that actual stuff? So the, the start that you joined did w what, what sort of was the origin of the technology that you were working on? I assume it came out of a lab somewhere. I, yeah, I was involved in two startups. One was after that epiphany moment in my PhD work, I basically threw out the work we were doing, and shifted gears and ended up developing the technology. That was the basis for, for actually a solar startup thinking about sort of thin-film, nanocrystal based, solar cells, Basically realizing that the, that the lifetime was so important, we just threw out all of the organics that we were working on and focused on. Like, you basically just need a new manufacturing approach to make something that looks like a traditional solar. So, that was a company that I kind of helped establish, but then ultimately didn't go. I was, I was meant to be sort of the founding, you know, grad student turn CTO. and then, for a number of reasons, didn't end up jumping into that as a startup and instead, through. Just some of the serendipity of being in the Bay area and Silicon Valley ended up, on the founding team of a battery startup that came out of another research lab at Berkeley. and this was funded by, Samira and Vanessa who, when, when coastal ventures was just going to start it. yeah, so, so like let's so. W when we say coming out of a lab, I think it's actually worth almost disecting what that means. Cause I suspect that it means different things to different people. and so, so someone in the lab. Did some research, figure it out. Okay. We think we can extend it was, it was a lifetime, et cetera, extended battery lifetimes, or, this was about making or energy batteries, higher energy density, batteries that were still safe and stable. using basically solid electrolytes. so, so they like publish her paper, like, like I assume that there's like, like they do some experiments. They come up with like the core. sort of process improvement. It's like, okay, we, we make batteries this, this old way, and now we need to make batteries at different way that will eventually make the battery into something useful. then what did, like, what did they need to do? What do you, what did you all do? Yeah, the origin story of CEO is I think a great one. So ingredients in this case and, and some, and there are some universal, I think things that you can pull out of this, you had a couple of graduate students and a professor at Berkeley, Natasha Bulsara, doing research, basically a polymer expert who starts doing research in terms of how polymers can be applied to batteries. the, the business as usual or the incentive structures within universities generally, you know, would say for Natasha to be successful in his career, he needs to make some new discoveries. He needs to write some great papers. he needs to advance, you know, as an academic, right. And he was doing that. and. In this case, it took this moment where, you know, Natasha was a dreamer and had, you know, just had a sense of, well, wait a second, I want this to be useful. I think this can be useful. He kind of had a zero with order idea that there's this problem in batteries, where, you know, you can, if you try and use high energy density, electrodes, like lithium metal, they can short across and lithium metals, flammable and combustible. And so, you know, There's this idea that you could make a high energy density battery. Unfortunately, it starts to look more like a bomb than a battery. and he, you know, to zero with order, the polymers that he's making could solve that problem, right. It could be robust and strong mechanically and still be highly conductive, for ions and. Tasha to his credit is audacious enough to say, Oh, and this is a time to, we have to recognize when venture capitalists are interested in funding these things at the early stages. Right? So it takes Natasha being audacious enough to say, I think we can, we can start something. And then it takes someone in this case, like the node who is as audaciou

In this conversation I talk to Luke Constable about the complicated tapestry of finance, funding projects, incentives, organizational and legal structures, social technologies, and more. Luke is the founder of the hedge fund Lampa Capital and publishes a widely-read newsletter full of fascinating deep dives. He's also trained as a lawyer and historian so he looks at the world with a fairly unique set of lenses. Disclaimer: nothing Luke says is an offer to buy or sell a security or to make an investment Links Luke on Twitter Lampa Capital Theory of Investment Value (John Burr Williams) 1,000 True Fans (Kevin Kelly) Quantum Country Patreon Lampa Capital's Open Questions The Empire of Value (André Orléan) Who Gets What and Why (Alvin Roth) The Mystery of Capital (Hernando de Soto) I, Pencil (Leonard Read) The Crime of Reason (Robert Laughlin) Andrew Lo's papers Transcript 0:01:05 BR: So if technology creates a lot of wealth, why does it feel like most people in finance are hesitant to invest in technology?   0:01:19 Luke Constable: So that's an interesting place to start. I think you have to understand, no one invests in technology. If you think about investors, investors invest in businesses that use technology, and so that's probably the first frame I would use. Investors aren't hesitant to invest in technology, investors never invest in technology. What investors do is they invest in these products that are going to generate cash flow streams, and so that's sort of the first thing. And then the second thing is, a lot of the technologies that you and I think about, they seem obvious at a macro scale, where you take a high level view and you say, "Well, it would be so much better if we had a blank sheet of paper," and I said, "We should do X."   0:02:10 LC: For instance, you could make an argument about housing technology in San Francisco, and you could say, "All of these houses built in SF, they're old Victorians, they don't really have washing machines and laundry machines, you could probably change the structural engineering, probably build them higher". And if you look at them and said, "Oh, I have a better prefab housing technology," or "I have a better way to do it," you'd miss the point, which is just because you've invented the physics, and this is the other thing, you actually have to sell it into a market. You have to work within the market, and so that's usually where I see a lot of the interesting technical products fall down.   0:02:53 BR: So the thing that I want to poke at in the assertion that people invest in businesses is that people invest in things that are not businesses as well, people invest in gold, in currencies and other, I guess, assets would be the high level thing, and so I guess the question is why isn't technology itself an asset, and there's probably a very obvious answer to this, I just...   0:03:25 LC: Sure, so let's take a step back and talk about the various asset classes, there's sort of a couple of ways to break them down.   0:03:32 BR: Okay.   0:03:33 LC: One way people do this is they'll say there are real assets, these are things like real estate, some people put commodities in there, and then there are sort of these yield assets, these are debt that is putting out a cash flow stream, and then you have equities, and there's some argument that cryptocurrency is sort of its own asset class, and then currencies might be their own asset class too. And what you'll quickly find is these things kind of blend together. A lot of them are different ways of financing sort of the same project. And then you have the ones that are just traded for their own sake. So there's sort of two questions you're asking, the first is, why isn't "technology" the same as like gold or silver or real estate, for instance? And so there's a use value to all of those commodities, and that's why they have value, and that actually is a cash flow stream, we actually do use gold, we do use silver, and that's how that works.   0:04:43 LC: But if you think about what's valuable, there's sort of something that's value... And I should have started with this. When you think about what value is, there's value in exchange and then there's value in use. So the value in exchange ones, these are often, you could argue, cryptocurrency or a lot of currencies, gold is actually usually thought of as a medium of exchange, that actually is valuable for cash flow purposes just probably not in the ways that you think. So what happens with these currencies and these stores of value is they sort of become Schelling points where I just know there are enough people transacting in that thing that I can find the liquidity, I can actually go convert to cash, and I can go basically get that cash when I need it. That actually is a cash flow need. It's just not often thought of that way.   0:05:40 LC: Now, liquidity is really valuable because you might be invested in the best business of all time, and it might have a very, very, very high net present value and be doing a lot of good for the world. But if you take a step back and say, "Wait a second, I have to pay off student loans," or "I have to pay off my mortgage," or "I just want some cash to go on vacation" or whatever you want to do with it, you look at this and say, "Gosh, I do need some liquidity," and that's what those other sort of trading assets are for.   0:06:10 BR: So basically, technology contributes to the use value of an equity asset, is that the right way to think about it?   0:06:22 LC: I don't think of technology that separate from... It's sort of so baked into the environment that it's just difficult to disentangle. Technology, lazily put, is just ways of doing things hopefully more efficiently than we're already doing them. And so if you think about why certain assets become tradable, either they're creating these cash flow streams, or there is some value in exchange. I mean, the way that I often frame investing for the people who I invest for is there's sort of two sets of flows that determine an asset's price. There is underlying asset's cash flows and then there are the capital flows of all the investors. So you have sellers for some reason, maybe they have liquidity needs, maybe they can't hold an asset for a regulatory reason or a legal reason, and then you have buyers who come in, because they're interested in that asset, and it could be because they think it's an interesting thing to invest in, it could be because the regulators told them that they have to buy it, it could be... You laugh, but this is actually...   0:07:32 BR: What sort of things do regulators mandate that people buy?   0:07:37 LC: Sure, so if you go look at banks and sovereign debt, well, actually banks and all debt. So you have the bank regulators set risk weightings on various types of debt, which is sort of a nice way of saying, there are all of these different cash flow streams, and the regulators are saying to you that certain cash flow streams are riskier or less risky. And shockingly, they often argue that their sovereign debt is less risky than some other cash flow streams.   0:08:13 BR: I'm shocked.   0:08:14 LC: In practice, that may or may not be true. It's a weird thing to think about, but, in some cases, a multi-national corporation might actually be a better credit than a country. But that's not how these things work, and so what happens is a bank regulator will sometimes go to a bank and say, "The risk weighting on the sovereign debt is far lower than the risk weighting on this corporate debt," which effectively is pushing the bank to go buy a certain type of debt, which then goes and funds all of those projects. So then coming back to all of this, if you think about investing in sort of these two sets of flows, like that underlying asset's cash flows and then the capital flows of all the investors, you basically, in practical terms, want to think about markets in terms of what's driving someone's action.   0:09:05 LC: And when you think about that, that's when market prices start to make sense. They won't make sense to you if you think that you're just going to sit down and solve an analytical equation where you just sort of put in a few inputs, you make a few estimates and then the price gets spit out. It's much more of a socially constructed thing.   0:09:25 BR: And going back to your point about liquidity, it feels like there's this... I don't know how to describe it, like sort of a weird effect where it feels like there's a consensus that investing in... I won't say technology, I'll say investing in a business that is proposing to build a technology with a very long-term time scale, there's consensus that that will eventually create something... Will eventually create a lot of value, but then at the same time, because of these liquidity constraints, very few people are doing that, and that's the argument for why people are not making those investments, but it seems like that would be a point where you could arbitrage. It seems like there should be some people who are willing to not get cash flow for a couple of decades, and they would be able to reap the rewards of making these sorts of investments, but you don't see that, so I assume that those people are smarter than I am. And so the question is, why don't you see people doing that?   0:10:50 LC: So you actually do see people doing this literally all the time, but it's not for the sexy technology concepts that you are thinking of. So go look into the public markets right now. You'll see a handful of software businesses that are trading at very high multiples to sales. So the idea is that you sort of have this trade-off: you could get free cash flow after taxes right now, or effectively more free cash flow down the line from some company that's growing quickly, and so what you do is you pay some price based on that free cash flow multiple. What happens when the free cash flow is really, really far down the line

In this conversation I talk to Donald Braben about his venture research initiative, peer review, and enabling the 21st century equivalents of Max Planck. Donald has been a staunch advocate of reforming how we fund and evaluate research for decades. From 1980 to 1990 he ran BP's venture research program, where he had a chance to put his ideas into practice. Considering the fact that the program cost two million pounds per year and enabled research that both led to at least one Nobel prize and a centi-million dollar company, I would say the program was a success. Despite that, it was shut down in 1990. Most of our conversation centers heavily around his book "Scientific Freedom" which I suspect you would enjoy if you're listening to this podcast. Links Scientific Freedom Transcript audio_only [00:00:00]   This conversation. I talked to Donald breathing about his venture research initiative, peer review, and enabling the 21st century equivalent of max Planck. Donald has been a staunch advocate for forming how we fund and evaluate research for decades. From 1980 to 1990, he ran BP's venture research program. Where he had a chance to put his ideas into practice. [00:01:00] Considering the fact that the program costs about 2 million pounds per year and enabled research, that book led to at least one Nobel prize and to send a million dollar company. I would say the program was success, despite that it was shut down in 1990. Most of our conversations centers heavily around his book, scientific freedom, which just came out from straight press. And I suspect that you would enjoy if you're listening to this podcast. So here's my conversation with Donald Raven.     would you explain, in your own words, the concept   of a punk club and why it's really well, it's just my name for the, for the, outstanding scientists of the 20th century, you know, starting with max blank, who looked at thermodynamics, and it took him 20 years to reach his conclusions, that, that matter was, was quantized. You know, and that, and, he developed quantum mechanics, that was followed by Einstein and Rutherford and, and, and a [00:02:00] whole host of scientists. And I've called, in order to be, succinct Coley's they, these 500 or so scientists who dominated the 20th century, the plank club. So I don't have to keep saying Einstein rather for that second. I said, and it's, it's an easy shorthand. Right. And so, do you think that like, well, there's a raging debate about whether the existence of the plank club was due to sort of like the time and place and the, the things that could be discovered in physics in the first half of the 20th century versus. Sort of a more or more structural argument. Do you, where do you really come down on that? The existence of the plank club? [00:03:00] W well, like, yeah, so like, I guess, I guess it's, tied to sort of like this, but the question of like, like almost like, yeah. Are you asking, will there be a 20th century, 21st century playing club? Do you think, do you think it's possible? Like, it's sort of like now right now. No, it's not. because, peer review forbids it, in the early parts of the 20th century, then scientists did not have to deal with, did not necessarily have to deal with peer review. that is the opinions of the, of the expert of the few expert colleagues. they just got on, on, Edgar to university and had a university position, which was as difficult then as it is now to get. But once you got a university position in the first part up to about 1970, then you could do then providing your requirements were modest, Varney. You didn't [00:04:00] need, you know, huge amounts of money. Say. You could do anything you wanted and, you didn't have to worry about your, your peers opinions. I mean, you did in your department when people were saying, Oh, he's mad. You know, and he's looking at this, that, and the other, you could get on with it. You didn't have to take too much attention. We pay too much attention to what they were doing, but now in the 21st century, consensus dominates everything. And, it is a serious, serious problem. Yeah. So I, I seriously believe that keeps me what keeps me going is that it is possible for there to be a plane club in the 21st century. It is possible, but right now it won't take, it won't happen. I mean, re there's been reams written on peer review, absolute huge, literature. and the, but, but most of it seems to have been written by, by people who at least favor the status [00:05:00] quo. And so they conclude that peer review is great, except perhaps for multidisciplinary research, which ma, which might cause problems. This is the establishment view. And so they take steps to try to ease the progress of multidisciplinary research, but still using peer review. Now. Multidisciplinary research is essentially is, is absolutely essential to venture research. I mean, because what they are doing, what every venture researchers, the researcher is doing is to look at the universe. and the world we inhabit in a new way. So that's bound to create new, new disciplines, new thought processes. And so the, when the conventional P, when the funding agencies say, there's a problem with multidisciplinary research, they're saying that's a problem with venture reserves. Yeah. And so therefore we won't have a plank club until that problem is [00:06:00] solved. And I proposed the solution in the book. Of course. Yeah, exactly. And so I guess, so with the book, I actually think of it as it's just like a really well done, an eloquent, almost like policy proposal, like it's, it's like you could, I feel like you could actually take the book and like hand it to. A policymaker and say like do this, I guess you could, so, I guess to put it, but like clearly nobody's done that. Right? did you, do you ever do that? Like, did you actually like go to,  government agencies or even  billionaires? Like the, the amount of money that you're talking about is almost like shockingly small. what, what are, what are people's responses of like, why not do this? Patrick Collison as being the only billionaire who has responded, I've met about, I don't know, half a dozen billionaires. And, they all want to, they all want to do things [00:07:00] their way, you know, they all want to, which is fair, which is fair enough. They all want to, sees a university through their own eyes. They are not capable of saying opening their eyes and listening to what scientists really want to do and to get what scientists really want to do. You've got you. You just can't just ask them straight off. You've got to talk to them. For a long time before they will reveal what they want really want to do. And then only a few of them will be capable of being a potential member of the plank club of the 21st century state. But it's a wonderful process. It's exciting. And I don't know why. well, I, I think I do actually, why the conventional authorities do not do this. And I believe that for, the reason this is more or less as follows that, for 20, 30 years following the expansion of the universities in about, about 1970 for political reasons. [00:08:00] no, not at all for, for scientific reasons that, there was a huge expansion in the universities and, and, and a number of academics. I really really mean it's factors of three, two, three, four, or something like that, depending on the country. Really huge. And, so therefore the old system where freedom for everyone was more or less guaranteed, which is what I would advocate freedom for everyone as a right. So, what we have done now is to develop absolute selection, rules, absolute selection rules for selecting venture researchers. And, and, and that's taken, you know, that's taken some time to develop them, but they work well. And, and, and open up the world to a complete ways, new ways of looking at it. Yeah, look, I mean, the, the, the track record seems very like very good, right? Like you, you, you, you [00:09:00] enabled research that would not have happened otherwise and led to Nobel prizes. Right. Like, I don't, I don't see how it could, what evidence one could present that your method works more so than that. and so it's, so yeah. Well, well, over the years you see, the, the scientists to work in for, for the funding agencies. they have advised politicians on the ways to ration research without affecting it. And they have come up with the way, the method of peer review, which is now a dairy girl, you know? it's absolutely essential. Yeah. Every to every funding agency in the world, I've not come across one that does not use it well, apart from our own operation, of course we don't use it. but we, we find ways around it. And that's the conventional wisdom is that there are no ways around, [00:10:00] there are no way. peer review is regarded as the only way to ensure research excellence. People keep saying that it's the only way, but we have demonstrated with the BP venture to search you and this and that UCL, that there is another way. And, and I guess so is, is, is the response from, people that you would propose this to simply that , they, they don't believe that.  they don't believe that it can work because it doesn't, it isn't peer reviewed, , is that the main contention? Any, any ideas now must, must, must survive. Peer review and venture research of course would not. And so therefore what we're saying is therefore not admissible. And now a few people, in like the 50 or so of my, my, of my supporters, very senior supporters, re regard what we [00:11:00] are doing as essential, but their voice is still tiny compared with the, you know, the millions of, researchers and, and the, I I'm the funding agencies. Now the funding agencies kept on saying that they have advised politicians over the years, that the only way to ensure to ensure, that the, that the scientific enterprise is healthy is to, is to, is to a DIA to peer review. Now. They cannot. They cannot now say, ah, yes, Raven po

A conversation with Adam Marblestone about his new project - Focused Research Organizations. Focused Research Organizations (FROs) are a new initiative that Adam is working on to address gaps in current institutional structures. You can read more about them in this white paper that Adam released with Sam Rodriques. Links FRO Whitepaper Adam on Twitter Adam's Website Transcript [00:00:00]   In this conversation, I talked to Adam marble stone about focused research organizations. What are focused research organizations you may ask. It's a good question. Because as of this recording, they don't exist yet. There are new initiatives that Adam is working on to address gaps. In current institutional structures, you can read more about them in the white paper that Adam released recently with San Brad regens. I'll put them in the show notes. Uh, [00:01:00] just a housekeeping note. We talk about F borrows a lot, and that's just the abbreviation for focus, research organizations. just to start off, in case listeners have created a grave error and not yet read the white paper to explain what an fro is. Sure. so an fro is stands for focus research organization. the idea is, is really fundamentally, very simple and maybe we'll get into it. On this chat of why, why it sounds so trivial. And yet isn't completely trivial in our current, system of research structures, but an fro is simply a special purpose organization to pursue a problem defined problem over us over a finite period of time. Irrespective of, any financial gain, like in a startup and, and separate from any existing, academic structure or existing national lab or things [00:02:00] like that. It's just a special purpose organization to solve, a research and development problem. Got it. And so the, you go much more depth in the paper, so I encourage everybody to go read that. I'm actually also really interested in what's what's sort of the backstory that led to this initiative. Yeah. it's kind of, there's kind of a long story, I think for each of us. And I would be curious your, a backstory of how, how you got involved in, in thinking about this as well. And, but I can tell you in my personal experience, I had been spending a number of years, working on neuroscience and technologies related to neuroscience. And the brain is sort of a particularly hard a technology problem in a number of ways. where I think I ran up against our existing research structures. in addition to just my own abilities and [00:03:00] everything, but, but I think, I think I ran up against some structural issues too, in, in dealing with, the brain. So, so basically one thing we want to do, is to map is make a map of the brain. and to do that in a, in a scalable high-speed. Way w what does it mean to have a map of the brain? Like what, what would, what would I see if I was looking at this map? Yeah, well, we could, we could take this example of a mouse brain, for example. just, just, just for instance, so that there's a few things you want to know. You want to know how the individual neurons are connected to each other often through synopsis, but also through some other types of connections called gap junctions. And there are many different kinds of synopsis. and there are many different kinds of neurons and, There's also this incredibly multi-scale nature of this problem where a neuron, you know, it's, it's axon, it's wire that it sends out can shrink down to like a hundred nanometers in [00:04:00] thickness or less. but it can also go over maybe centimeter long, or, you know, if you're talking about, you know, the neurons that go down your spinal cord could be meter long, neurons. so this incredibly multi-scale it poses. Even if irrespective of other problems like brain, computer interfacing or real time communication or so on, it just poses really severe technological challenges, to be able to make the neurons visible and distinguishable. and to do it in a way where, you can use microscopy, two image at a high speed while still preserving all of that information that you need, like which molecules are aware in which neuron are we even looking at right now? So I think, there's a few different ways to approach that technologically one, one is with. The more mature technology is called the electron microscope, electromicroscopy approach, where basically you look at just the membranes of the neurons at any given pixel sort of black or white [00:05:00] or gray scale, you know, is there a membrane present here or not? and then you have to stitch together images. Across this very large volume. but you have to, because you're just able to see which, which, which pixels have membrane or not. you have to image it very fine resolution to be able to then stitch that together later into a three D reconstruction and you're potentially missing some information about where the molecules are. And then there's some other more, less mature technologies that use optical microscopes and they use other technologies like DNA based barcoding or protein based barcoding to label the neurons. Lots of fancy, but no matter how you do this, This is not about the problem that I think can be addressed by a small group of students and postdocs, let's say working in an academic lab, we can go a little bit into why. Yeah, why not? They can certainly make big contributions and have to, to being able to do this. But I think ultimately if we're talking about something like mapping a mouse brain, it's not [00:06:00] going to be, just a, a single investigator science, Well, so it depends on how you think about it. One, one, one way to think about it is if you're just talking about scaling up, quote, unquote, just talking about scaling up the existing, technologies, which in itself entails a lot of challenges. there's a lot of work that isn't academically novel necessarily. It's things like, you know, making sure that, Improving the reliability with which you can make slices of the brain, into, into tiny slices are making sure that they can be loaded, onto, onto the microscope in an automated fast way. those are sort of more engineering problems and technology or process optimization problems. That's one issue. And just like, so Y Y Can't like, why, why couldn't you just sort of have like, isn't that what grad students are for like, you know, it's like pipetting things and, doing, doing graduate work. So like why, why couldn't that be done in the lab? That's not why [00:07:00] they're ultimately there. Although I, you know, I was, I was a grad student, did a lot of pipetting also, but, But ultimately they're grad student. So are there in order to distinguish themselves as, as scientists and publish their own papers and, and really generate a unique academic sort of brand really for their work. Got it. So there's, there's both problems that are lower hanging fruit in order to. in order to generate that type of academic brand, but don't necessarily fit into a systems engineering problem of, of putting together a ConnectTo mapping, system. There's also the fact that grad students in, you know, in neuroscience, you know, may not be professional grade engineers, that, for example, know how to deal with the data handling or computation here, where you would need to be, be paying people much higher salaries, to actually do, you know, the kind of industrial grade, data, data piping, and, and, and many other [00:08:00] aspects. But I think the fundamental thing that I sort of realized that I think San Rodriquez, my coauthor on this white paper also realized it through particularly working on problems that are as hard as, as clinic Comix and as multifaceted as a system building problem. I th I think that's, that's the key is that there's, there's certain classes of problems that are hard to address in academia because they're system building problems in the sense that maybe you need five or six different. activities to be happening simultaneously. And if any, one of them. Doesn't follow through completely. you're sort of, you don't have something that's novel and exciting unless you have all the pieces putting, you know, put together. So I don't have something individually. That's that exciting on my own as a paper, Unless you, and also three other people, separately do very expert level, work, which is itself not academically that interesting. Now having the connectome is academically [00:09:00] interesting to say the least. but yes, not only my incentives. but also everybody else's incentives are to, to maybe spend say 60% of their time doing some academically novel things for their thesis and only spend 40% of their time on, on building the connectome system. Then it's sort of, the probability of the whole thing fitting together. And then. We see everyone can perceive that. And so, you know, they basically, the incentives don't align well, for, for what you would think of as sort of team science or team engineering or systems engineering. yeah. And so I'm like, I think, I think everybody knows that I'm actually like very much in favor of this thing. So, I'm going to play devil's advocate to sort of like tease out. what I think are. Important things to think about. so, so one sort of counter argument would be like, well, what about projects? Like cert, right? Like that [00:10:00] is a government yeah. Led, you should, if you do requires a lot of systems engineering, there's probably a lot of work that is not academic interesting. And yet, it, it, it happens. So like there's clearly like proof of concepts. So like what what's like. W why, why don't we just have more things like, like certain for, the brain. Yeah. And I think this gets very much into why we want to talk about a category of focused research organizations and also a certain scale, which we can get into. So, so I think certain is actually in many ways, a great example of, of this, obviously this kind of team science and team engineering is incredible. And there are many ot

Michael Filler and Matthew Realff discuss Fundamental Manufacturing Process innovations. We explore what they are, dig into historical examples, and consider how we might enable more of them to happen. Michael and Matthew are both professors at Georgia Tech and Michael also hosts an excellent podcast about nanotechnology called Nanovation. Our conversation centers around their paper Fundamental Manufacturing Process Innovation Changes the World. If you're in front of a screen while you're listening to this, you might want to pull up the paper to look at the pictures. Key Takeaways Sometimes you need to go down to go back up The interplay between processes and paradigms is fascinating We need to spend more time hanging out in the valley of death Links Fundamental Manufacturing Process Innovation Changes the World(Medium)(SSRN) Michael on Twitter Matthew Realff's Website Michael Filler's Website Nanovation Podcast Topics - The need for the innovator to be near the process - Continuous to discrete shifts - Defining paradigms outlines what progress looks like - Easy to pay attention to artifacts, hard to pay attention - Hard to recreate processes - The 1000x rule of process innovations - Quality vs price improvements - Process innovation as a discipline - Need to take a performance hit to switch paradigms - How to enable more fundamental manufacturing process innovations Transcript [00:00:00] this conversation, I talked to Michael filler and Matthew Ralph about fundamental manufacturing process innovations. We explore what they are, dig into historical examples and consider how we might enable more of them to happen. Michael and Matthew are both professors at Georgia tech and Michael also hosts an excellent podcast about nanotechnology called innovation. Our conversation centered around their paper called fundamental [00:01:00] manufacturing process. Innovation changes the world, which I've looked to in the show notes and highly recommend the fact that they posted it on medium. In addition to more traditional methods, give you a hint that they think a bit outside the normal academic box. However, I actually recommend the PDF version on SSRN, which is not behind a paywall only because it has great pictures for each process that I found super helpful. If you're in front of a screen, while you're listening to this, I suspect that having them handy, it might enhance the conversation. And here we go. the, the place that I'd love to start is, to sort of give everybody a, get them used to both of your voices and sort of assign a personality, a personality to each of you. so if each of you would say a bit about yourselves, and the. The, the sort of key bit that I've loved you to say is to, to focus on something that you believe that many people in your discipline would sort [00:02:00] of cock an eyebrow at because clearly by publishing this piece on medi you sort of identify yourself as not run of the mill professors.   Oh boy. Okay. So we're going to start juicy, real juicy. So I guess I'll go since I'm speaking, this is Mike filler speaking. Great to be here. so I've been a professor of chemical engineering at Georgia tech for a little over 10 years now. my research group works in nanoscale materials and device synthesis and scale up. So for say electronics applications, Yeah. I mean, this article, which we'll talk about emerged from, you know, can I say a frustration that I had around electronics really is where it started for me, at least, that. We have all this focus on new materials or new device physics or new circuit. And I know your listeners are probably thinking about morphic computing or quantum computing, and these are all very cool things, but it seemed to me [00:03:00] that we were entirely missing the process piece. The, how do we build computers? and, and, and circuitry. And, and so that's where this started for me was, starting to realize if we're not dealing with the process piece, that we're, we're missing a huge chunk of it. And I think one of the things is that people, people miss that where within working within the context of something developed 50 or 60 years ago, in many cases, and it's it's was really hidden to a lot of people. And so that, that was where I came at this. Great. All right. So, yeah, so I'm, also a professor of chemical and biomolecular engineering at Georgia tech. my background is actually in process systems engineering. And, if you go back to the late 1960s, early 1970s, actually frankly, before I was a much more than in shorts, there was a, that was a real push towards. The role of process systems engineering in [00:04:00] chemical engineering in it really arose with the, with the advent of computing and the way that computing could be used to help in chemical engineering. And then slowly over time, the, the role of process systems engineering has become, I think, marginalized within the chemical engineering community, it's gone much over towards. What I call science and engineering science in a way from the process systems piece of it. And so, you know, as Mike would, would berate me with the, with his travails over, over what he was trying to do with nano integration and nanotechnology, I realized that what he was doing was describing a lot of the same frustrations I felt with the way that process systems engineering was being marginalized and pushed to the edges of chemical engineering with the. Focus more around fundamental discoveries rather than actually how we translate those fundamental discoveries, into, functioning, processes that then lead to outcomes that affect society. So for me, it, it, it [00:05:00] was a, it was a combination of, talking to Mike and then my own frustrations around how my own field was somewhat marginalized within the context of chemical engineering. Got it. And, sort of to, to anchor everybody and, and start us off. could you just explain what a fundamental manufacturing process innovation it's. So the way we think of fundamental process innovation or manufacturing process innovation is actually rethinking how the steps in a process are organized and connected together. And so that has become the paradigm which we have. we have set for fundamental manufacturing process innovation, and these innovations come in in different categories that enable us to put these processes together. And one of the examples of which for example, is. I'm factoring taking something that has been done together at one process step and separating it into two different steps that occur maybe at different [00:06:00] times or in different places. And by so doing, we actually enable us to make, a tremendous change in the way that that process operates. So it's really around. The strategy for organizing and executing the manufacturing steps and using a set of schema is to sort of understand how over history we have been able to do that. Do you want to add to that mic? Yeah. I want to take a step back outside of manufacturing. So one of the examples we give at the outset of the piece is not in manufacturing, but in shopping something that every single person listening to this can wrap their mind around, I think. and I still love the example cause it just kind of. I miss it every single day. and this is all pre COVID thinking of course, but the idea that say a hundred years ago, and a lot or Western societies, you would go to let's call it the general store. and you'd walk in, go up to the counter. And, if I have a list maybe, and you'd handle lists to the purveyor, and they would go [00:07:00] in the back rows of shelves and they'd pull off what was on your list and they'd bring it out to you, you pay for it and you go on your Merry way. And then, you know, several decades ago, this started to change, probably half century my ex ex ex. Exactly sure. The timing, but, to, to a model, where instead of a single shop keeper, having to interface with many individual, shoppers, it was now many shoppers who did the traversing of those aisles themselves, right? This is at least in Western society is what we are familiar with today as the grocery store or the target or the Walmart. And what you do is you. Trade one thing for another in doing that right. Instead of, the person, the, the purveyor, getting things for you, which from a customer's perspective is very nice. Right? you, you, you no longer have that, right. You're being told. Okay. He used to, yeah, he or she used to get it for you now. You're going to go and traverse the ALS yourself. But you do get something in return as the [00:08:00] shopper. And that is a lower costs because now one store at the same time can be, open to many, many people stopping shopping simultaneously. So, selection goes up, costs go down and there's a benefit for the customer, and the shopkeeper. So this is an example of a process innovation it's the it's still shopping, but it, it takes the old process paradigm and inserts a new one. Excellent. And so you, in your paper, you illustrate eight major historical, fundamental process innovations. And I would love to sort of frame the conversation by walking through them so that, a just because they're great history and B, so that everybody can sort of be anchored on the very concrete, examples while at the same time, I'll, I'll sort of poke at, The, the more sort of abstract questions and ideas around this. so the, the first, [00:09:00] the first one you talked about is the shift from the new Komen to the watt steam production process. So like, what was that? And, and why was that important? it was important because, what it did was it changed fundamentally how we could make power. So the newcomer engine had, the condensation of steam in the same vessel, as, as the, as what was being the vacuum was being pulled to enable the, Pulling of water up from the coal mines in Britain, turns out it's actually 10 mines rather than coal mines, where this was first developed. And what, what did was to factor tha